Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 31 appraisals
Rheumatic diseases clinics of North America
Demystifying Artificial Intelligence: Key Concepts with Examples in Rheumatology
Artificial intelligence (AI) refers to a broad class of computational methods to perform tasks that typically require human intelligence, such as learning patterns, reasoning, and problem solving. AI is increasingly applied across rheumatology research and clinical practice to analyze complex data and support clinical decision-making, yet its underlying concepts are often perceived as opaque or inaccessible. This aim of this article is to demystify foundational AI concepts by defining core terminology and describing major learning paradigms and analytical methods, highlighted through clinically relevant examples from rheumatology. We explain how modern AI models differ from traditional analytical approaches.
2 Aug 2026
Read appraisal →PloS one
Does delayed exercise-based priming improve subsequent athletic performance? A systematic review and multilevel meta-analysis.
This systematic review and multilevel meta-analysis examined whether delayed exercise-based priming improves subsequent athletic performance. Following PRISMA 2020 principles, 3,095 records were identified; after removing 1,107 duplicates, 1,988 unique records were screened, 212 full-text reports were assessed, 186 reports were excluded at eligibility, and 26 studies were included. Eighteen studies contributed 46 effect sizes to the quantitative synthesis. Risk of bias was assessed with RoB 2 principles calibrated for crossover exercise trials, and certainty was evaluated using GRADE. A three-level random-effects model showed a small positive average effect of delayed priming on subsequent performance (g = 0.232, 95% CI [0.092, 0.372], p = .002), with cluster-robust inference supporting the same conclusion (p = .004). The prediction interval crossed zero (-0.265 to 0.730), and the overall certainty of evidence was low. Delayed exercise-based priming can be used as a practical competition-day strategy when embedded in planned preparation windows, although protocol selection and athlete response should be checked in training before competition use.
31 July 2026
Read appraisal →Lasers in medical science
Effect of photobiomodulation on trapezius myofascial pain: a randomized, sham-controlled, double-blind clinical trial.
To investigate the effect of 16 J of 808 nm low-level laser therapy (LLLT) on pain in individuals with trapezius trigger points (TPs). Randomized, double-blind, controlled trial conducted with 52 adults (≥ 18 years) with chronic trapezius pain and TPs. Participants were assigned to the LLLT group (808 nm, 16 J per TP) or the sham group (same protocol without emission). Both received eight sessions over four weeks. Outcomes included pain perception, number of TPs, cervical function, skin temperature, global perceived effect, and adverse events. Randomization was concealed from evaluators and participants. Repeated measures ANOVA (SPSS 21) was used for analysis. No significant differences were found between groups for pain [F(2,100) = 1.21; p = 0.30, η2 = 0.024], TPs [F(1.09,54.27) = 0.35; p = 0.57, η2 = 0.007], cervical skin temperature [F(1.79,89.65) = 0.79; p = 0.45, η2 = 0.042], trapezius temperature [F(2,100) = 1.09; p = 0.34, η2 = 0.021], or global perceived effect [F(1.44) = 1.91; p = 0.17, η2 = 0.029]. Cervical function improved significantly in the LLLT group [F(1.36,67.73) = 3.75; p = 0.04, η2 = 0.070]. Adverse effects were mild and infrequent, mainly warmth. Both groups reported clinically relevant pain reduction. LLLT at 16 J per TP with 808 nm was not superior to placebo for pain, TP count, or skin temperature. However, it improved cervical function, suggesting a functional benefit. Registration: july 18, 2022 on the Brazilian Clinical Trials Registry (REBEC - RBR-10m474jv).
28 July 2026
Read appraisal →PloS one
Hamstring strength assessment methods in sport: A systematic review and meta-analysis of post-activity strength decrements, asymmetry benchmarks, and measurement reliability.
Hamstring strain injuries (HSI) represent one of the most prevalent and recurrent injuries in sport, making reliable strength assessment essential for neuromuscular monitoring in athletic populations. Despite the breadth of available testing modalities, no comprehensive synthesis of assessment methods, their reliability, post-activity strength responses, or normative asymmetry values has been undertaken. This systematic review and meta-analysis aims to evaluate the reliability of hamstring strength assessment methods, quantify the time-course of strength decrements following competition, establish normative inter-limb asymmetry benchmarks, and identify gaps to guide future assessment practices. PubMed, Scopus, SPORTDiscus, and Web of Science were searched from inception to November 1, 2025 following PRISMA guidelines. Methodological quality was assessed using a modified version of the Downs and Black checklist. A total of 161 studies were included in qualitative synthesis and 106 in quantitative analysis. Isokinetic dynamometry was the most frequently used modality, followed by force platform assessments and Nordic hamstring-based devices, with a clear upward trend in field-based testing observed after 2010. Multiple modalities demonstrate good reliability, sensitivity to post-activity fatigue, and comparable patterns to detect inter-limb asymmetries, supporting their use across varied applied settings. Hamstring strength deficits persisted for 24-72 hours following on-field activity, identifying a critical vulnerability window during congested schedules. Minor inter-limb asymmetries of approximately 3-4% appear normative and should not be considered clinically meaningful in isolation. Practitioners should establish individualized baselines and integrate routine monitoring with evidence-based recovery strategies to mitigate HSI risk throughout competitive seasons.
23 July 2026
Read appraisal →BMJ open
Current evidence on radiofrequency denervation for chronic low back pain: a systematic review and meta-analysis
OBJECTIVE: To assess the effects of radiofrequency (RF) denervation for chronic low back pain (LBP) from facet joint, sacroiliac joint, discogenic and non-specific pain. DESIGN: Systematic review and meta-analysis. DATA SOURCES: We searched Medline, Embase, Cochrane Library, CINAHL, PsycINFO, ClinicalTrials.gov and WHO ICTRP up to 18 February 2025 for randomised controlled trials (RCTs). STUDY SELECTION, DATA EXTRACTION AND SYNTHESIS: Pairs of reviewers independently selected studies, extracted data and assessed risk of bias (Cochrane RoB 2). Meta-analyses with clinically homogeneous studies were performed and certainty of evidence was assessed using Grades of Recommendation, Assessment, Development and Evaluation. RESULTS: 55 RCTs (n=4394) were included; many outcomes were at some risk of bias. Low to moderate certainty evidence suggested small improvements in pain and function with RF denervation versus sham for facet joint pain, sacroiliac, discogenic and non-specific chronic LBP. None exceeded predefined minimally clinically important between-group differences, suggesting limited clinical relevance. CONCLUSIONS: RF denervation showed small improvements in pain and function compared with sham, steroid injection or standard care, but the effects did not reach clinically meaningful thresholds and the certainty of evidence was very low to moderate. Additional research is needed to clarify the effects of RF denervation and guide clinical decision-making. PROSPERO REGISTRATION NUMBER: CRD42023460743.
18 July 2026
Read appraisal →Scientific reports
Integrating skeleton based representations for robust yoga pose classification using deep learning models
Yoga is a popular form of exercise worldwide due to its spiritual and physical health benefits, but incorrect postures can lead to injuries. Automated yoga pose classification has therefore gained importance to reduce reliance on expert practitioners. While human pose keypoint extraction models have shown high potential in action recognition, systematic benchmarking for yoga pose recognition remains limited, as prior works often focus solely on raw images or a single pose extraction model. In this study, we introduce a curated dataset, "Yoga-16", which addresses limitations of existing datasets, and systematically evaluate three deep learning architectures-VGG16, ResNet50, and Xception-using three input modalities: direct images, MediaPipe Pose skeleton images, and YOLOv8 Pose skeleton images. Our experiments demonstrate that skeleton-based representations outperform raw image inputs, with the highest accuracy of 96.09% achieved by VGG16 with MediaPipe Pose skeleton input. Additionally, we provide interpretability analysis using Grad-CAM, offering insights into model decision-making for yoga pose classification with cross validation analysis.
17 July 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Magnetically actuated microrobotic system for sequential treatment of biofilm
Biofilm-associated infections present a critical therapeutic challenge due to antibiotic resistance and impaired tissue healing. Here, we present a microrobotic system (MZ-8) that integrates real-time human-steered navigation with autonomous, microenvironment-responsive therapy to actively eradicate biofilms and promote tissue regeneration. This microrobotic system features a spine-inspired structure for mechanical biofilm disruption, a pH-responsive ZIF-8 coating for immunomodulatory Zn2+ release, and closed-loop actuation under second near-infrared fluorescence guidance. In a rat model of periprosthetic joint infection, MZ-8 achieved effective biofilm removal, induced a pro-regenerative immune response by polarizing macrophages toward the M2 phenotype, and significantly enhanced tissue regeneration. Transcriptomic analysis further revealed the activation of immunomodulatory pathways and upregulation of M2-associated genes, confirming the system's sequential shift from eradication to repair. Moreover, validation in a rabbit model and human knee joint confirmed its operational feasibility under clinical imaging guidance and excellent biosafety. This work establishes that integrating physical eradication, biochemical immunomodulation, and interactive control within a single system is essential for advancing from infection clearance to functional tissue restoration. Thus, it provides a therapeutic paradigm for biofilm-associated diseases and lays a foundation for future intelligent, clinically adaptive anti-infective systems.
15 July 2026
Read appraisal →International journal of medical informatics
Beyond the conventional: Artificial intelligence in identifying risk factors in sports injuries. A scoping review.
PURPOSE: To map contemporary uses of artificial intelligence (AI) to identify conventional and unconventional risk factors for sports injuries in athletes. METHODS: Following Joanna Briggs Institute guidance and PRISMA-ScR, we conducted an open-access search (January 2015-September 2025) in PubMed/MEDLINE, Scopus, and ScienceDirect using MeSH/DeCS terms for AI and injury risk. Records were managed in Rayyan with de-duplication, independent triple screening, standardized data extraction, and narrative synthesis to address heterogeneity. RESULTS: Fifty-nine studies met inclusion criteria. Frequently modelled predictors included sleep quality and psychological state, external and internal load metrics, and prior injury. AI approaches spanned classical machine learning and deep learning applied to multimodal sensor and clinical data. Across studies, generalizability was limited by heterogeneous populations, outcomes, and measurement/reporting practices; few works reported consistent measurement standards or external validation. Where assessed, observer agreement and classification performance were acceptable but variable. CONCLUSION: AI for sports-injury risk is expanding rapidly, led by classical machine learning on multimodal sensor data. Key gaps are external validity and reproducibility. Progress will require multicenter prospective cohorts, standardized measurement and reporting, sport-specific meta-analyses, transparent model sharing, and deliberate clinical integration into athlete care pathways.
4 July 2026
Read appraisal →Diagnostic and interventional imaging
Artificial intelligence in emergency musculoskeletal imaging: A critical review of current applications.
Artificial intelligence (AI) is increasingly shaping emergency musculoskeletal imaging, where rapid and accurate diagnosis is often challenged by high imaging volumes and time pressure. These constraints increase the risk of missed injuries and underscore the need for tools that support faster and reliable assessments. AI systems show promise in improving workflow efficiency by prioritizing urgent studies, guiding modality selection, and reducing reporting delays. Deep learning models can enhance abnormality detection by identifying fractures, soft tissue injuries, and infectious processes, and can provide structured classifications that support clinical decision making. Pediatric-focused AI systems address age-specific developmental considerations and offer valuable support for clinicians with varying levels of pediatric musculoskeletal expertise. Large language models further expand the role of AI by improving report clarity, generating structured impressions, and facilitating communication with clinicians, patients, and families. Despite these advances, challenges remain, including limited external validation, dataset bias, and medicolegal considerations. This review summarizes current AI applications across these domains and highlights key strengths, limitations, and future directions for safe and effective integration into emergency musculoskeletal imaging.
2 July 2026
Read appraisal →Oral health & preventive dentistry
Association Between Ankylosing Spondylitis and Periodontitis: A Systematic Review
PURPOSE: This systematic review aimed to investigate a potential association between ankylosing spondylitis (AS) and periodontitis (PD), two chronic inflammatory diseases. METHODS AND MATERIALS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, searches were conducted in PubMed, The Cochrane Library, Scopus, and Web of Science. Keywords included 'spondyloarthritis', 'ankylosing spondylitis', 'periodontal diseases', and 'periodontitis'. Study selection was performed by independent authors, and quality was assessed using Scottish Intercollegiate Guidelines Network (SIGN) methodology and the STROBE-MR statement. RESULTS: Out of 255 initial studies, 18 eligible studies (3 cohort, 14 case-control, 1 Mendelian randomisation) were selected, all of acceptable to high quality. The prevalence of PD in AS patients showed conflicting results. Periodontal parameters like bleeding on probing (BOP), probing pocket depth (PPD), and clinical attachment loss (CAL) didn't consistently show statistically significant differences but were frequently correlated with AS parameters (eg, MASES, BASDAI, BASFI, CRP, AS duration). AS treatment with anti-TNF-α agents was associated with a decrease in PPD and CAL. Other findings included an increase in Gingival Index in AS patients treated with anti-TNF-α and mixed results for the Decayed, Missing, and Filled Teeth Index. Microbiological studies showed inconsistent results for Porphyromonas gingivalis (Pg), but anti-Pg IgG levels correlated with AS parameters. PD appeared to significantly increase inflammatory markers (IL-6, TNF-α, CRP, ESR) in AS patients. Only one study investigated a genetic association, finding none. CONCLUSION: A definitive conclusion regarding an increased prevalence of PD in AS patients cannot be drawn with certainty from this review, possibly due to heterogeneous classification criteria. However, the correlation between PD and AS parameters, alongside the impact of AS therapy on periodontal health, is noteworthy. Screening and treating periodontal disease in AS patients might delay symptom progression and reduce drug dosage, though targeted clinical trials are necessary to confirm these findings.
26 June 2026
Read appraisal →BMJ open
Workplace interventions for chronic musculoskeletal disorders: a systematic review
OBJECTIVES: To provide an updated systematic review of the effectiveness of workplace interventions for chronic musculoskeletal disorders, identify key intervention components and assess their impact on health and work-related outcomes. DESIGN: Systematic review of randomised controlled trials. DATA SOURCES: MEDLINE, EMBASE, CINAHL, PsycINFO, Scopus and Web of Science were searched for studies published between 2018 and 2024. Grey literature sources were also consulted. ELIGIBILITY CRITERIA: Randomised controlled trials and cluster-randomised trials evaluating workplace interventions targeting adults with chronic musculoskeletal disorders were included. Outcomes of interest included pain, functional outcomes, quality of life and work-related outcomes (eg, work ability, productivity, presenteeism and absenteeism). DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened studies, extracted data and assessed risk of bias using the Cochrane Risk of Bias 2 tool. Due to heterogeneity, only a narrative synthesis was conducted. RESULTS: 23 studies involving 2456 participants were included and classified into physical exercise, ergonomic and multicomponent interventions. Physical exercise interventions consistently improved pain and functional outcomes. Digital delivery of exercise interventions demonstrated comparable effectiveness to face-to-face or workplace-based approaches. Multicomponent interventions, combining exercise with ergonomic strategies, demonstrated the most consistent benefits across outcomes. Evidence for work-related outcomes was limited and inconsistent, and quality of life was assessed in only a small number of studies. CONCLUSIONS: Workplace interventions, particularly multicomponent approaches combining exercise, ergonomics and education, can be effective in improving health outcomes in chronic musculoskeletal disorders. However, evidence on work-related outcomes remains limited and inconsistent. PROSPERO REGISTRATION NUMBER: CRD420261289218.
24 June 2026
Read appraisal →Spine
Differentiating Spinal Deformity Versus Leg Length Discrepancy in Patients With Pelvic Obliquity : Systematic Review of Diagnostic Methods, Imaging, and Clinical Management
STUDY DESIGN: Systematic review, PROSPERO ID: (CRD42023112392). OBJECTIVE: We aim to provide physicians with an evidence-based stepwise approach to distinguishing the primary cause of plane pelvic obliquity (PO) to guide treatment. SUMMARY OF BACKGROUND DATA: PO is a frequent clinical finding in both spinal deformity (SD) and leg length discrepancy (LLD) but distinguishing between these etiologies remains challenging due to overlapping clinical and radiographic features and potential multifactorial origins. Misdiagnosis may delay care and worsen patient outcomes. MATERIALS AND METHODS: PubMed, Embase, Scopus, and Web of Science were searched from inception to July 2025 for English-language studies evaluating coronal PO in the context of LLD and/or SD that explicitly compared or differentiated limb-origin from spine-origin obliquity using radiographic, advanced imaging, or validated clinical assessments and reported diagnostic accuracy, discriminative features, or management impact. Two reviewers independently screened studies, extracted data, and performed ROBINS-I risk assessment and Newcastle-Ottawa Scale quality assessment. Owing to marked heterogeneity in populations, measurement protocols, and thresholds, findings were synthesized qualitatively with stratification by age group where possible. RESULTS: Of 436 records, 24 cohort studies met the inclusion criteria. Standing anteroposterior (AP) radiographs reliably quantified both LLD and PO, while advanced imaging provided high reproducibility for subtle or ambiguous cases. Radiographic block or shoe lift correction tests distinguished limb-origin and spine-origin PO through immediate postural correction. Key discriminators included a lumbar curve or PO resolving with block/lift correction suggesting LLD, versus persistent deformity despite correction suggesting spinal etiology. Difficulty remains in diagnosing and treating mixed etiology patients. CONCLUSION: Accurate differentiation of spinal versus limb-driven PO begins with a structured diagnostic approach. AP radiographs, followed by functional block tests, and advanced imaging, may increase diagnostic confidence and decrease unnecessary interventions. Particularly in patients with both LLD and coronal plane SD, prospective studies are required to standardize measurement protocols and improve long-term outcomes. LEVEL OF EVIDENCE: Level IV.
16 June 2026
Read appraisal →British journal of sports medicine
Randomised three-armed trial investigation of the Copenhagen Achilles tendon Rupture Treatment Algorithm (CARTA) for individualised treatment of acute Achilles tendon rupture
OBJECTIVE: To evaluate whether individualised treatment using the Copenhagen Achilles Rupture Treatment Algorithm (CARTA) is superior to default operative or non-operative strategies. METHODS: We conducted a multicentre, three-arm, randomised controlled trial. Adults with acute Achilles tendon rupture were randomised (1:1:1) to individualised CARTA, non-operative or operative treatment. In the CARTA arm, surgery was indicated if ultrasound showed<25% tendon overlap or ≥7% elongation. The primary outcome was the Heel-Rise Work Test (HRWT) at 12 months. Secondary outcomes included HRWT at 6 months, and at both 6 and 12 months, the Heel-Rise Height, Achilles tendon Total Rupture Score (ATRS), Tegner activity scale, Copenhagen Achilles tendon Length Measure, Achilles Tendon Resting Angle (ATRA) and complication recordings. RESULTS: Between May 2018 and June 2023, 970 patients were screened; 300 were randomised (male/female 76%/24%, mean age 41 (SD 1)). At 12 months, data were available for 98 CARTA (64% operated), 100 non-operative and 97 operative patients. There were no significant between-group differences in HRWT at 12 months. However, CARTA significantly reduced rerupture rates by 73% compared with non-operative treatment (3% (95% CI) (1 to 8) vs 11% (6 to 19), p=0.03), improved ATRS by 8 points (1 to 15, p=0.02) and improved ATRA by -2° (-4 to -1), p=0.01). No difference between CARTA and operative treatment was found except 36% less underwent surgery. CONCLUSION: Individualised CARTA did not improve functional outcomes based on the HRWT at 12 months, but among secondary outcomes, it reduced rerupture rates and improved patient-reported outcomes compared with non-operative treatment and with fewer surgeries than routine operative care. TRIAL REGISTRATION NUMBER: NCT03525964.
14 June 2026
Read appraisal →European radiology experimental
Enhancing resolution and image quality in musculoskeletal MRI using deep learning reconstruction
OBJECTIVE: Deep learning-based noise reduction enhances image quality, overcoming the tradeoff among acquisition time, spatial resolution, and signal-to-noise ratio (SNR). We implemented deep learning reconstruction (DLR) into a 1.5-T musculoskeletal (MSK) magnetic resonance imaging (MRI) protocol to improve image quality without compromising SNR. MATERIALS AND METHODS: We retrospectively analyzed 39 MRI examinations performed on a 1.5-T scanner using standard-resolution (SR) sequences and sequences with higher resolution reconstructed with DLR (HR-DLR). Exams of the knees, shoulders, ankles, and hips were evaluated. The included sequences were: three-dimensional T2-weighted fast advanced spin-echo; T1-weighted and proton density-weighted fast spin-echo. One expert reader and two junior readers in agreement evaluated the visibility of various structures using a 5-point Likert scale in a blind manner. A fourth reader estimated the SNR and contrast-to-noise ratio (CNR) in bone and muscle. A mixed model was used to compare HR-DLR versus SR measures. The agreement between radiologists was assessed with the Kendall τ coefficient. RESULTS: The HR-DLR sequences globally had a smaller pixel size and shorter acquisition time. A good inter-reader agreement was obtained for SR sequences (0.613 ≤ τ ≤ 0.788) and even higher levels of agreement for HR-DLR sequences (0.682 ≤ τ ≤ 0.961). All the structures had higher or similar Likert scores in HR-DLR sequences (p < 0.001), regardless of joint and sequence contrast. Apparent SNR and CNR of HR-DLR and SR were similar. CONCLUSION: Incorporating DLR into 1.5-T MSK MRI protocols enhances resolution and maintains SNR and CNR, improving MSK structure visualization. RELEVANCE STATEMENT: This study demonstrated the effectiveness of deep learning reconstruction in improving the efficiency of 1.5-T musculoskeletal MRI exams. Despite shorter acquisition times, the visibility of key MSK structures was consistently rated as superior or similar in higher-resolution images with DLR. KEY POINTS: MRI is one of the primary diagnostic tools for evaluating MSK injuries and disorders. Deep learning reconstruction (DLR) implemented in a 1.5-T MSK protocol improved resolution, still preserving SNR and CNR. Higher scores were assigned by different raters to the DLR images, showing image quality improvement.
29 May 2026
Read appraisal →Archives of orthopaedic and trauma surgery
Loop versus anchor tenodesis of the long head of the biceps tendon: a prospective randomized controlled pilot study
BACKGROUND: The long head of the biceps tendon is a frequent source of anterior shoulder pain, often treated with tenodesis. While anchor tenodesis is well established, it carries risks of implant-related complications. This study evaluates an implant-free "loop tenodesis" technique as a potentially safer and cost-effective alternative. METHODS: In this prospective, double-blinded, randomized exploratory study, 48 patients undergoing arthroscopic biceps tenodesis were randomized to either loop (n = 24) or anchor (n = 24) tenodesis. Clinical outcomes were assessed using ASES, Constant, and DASH scores, as well as range of motion, supination strength, satisfaction, aesthetic appearance, and return-to-sport. Statistical analysis was performed using the Mann-Whitney U and Wilcoxon signed-rank tests to compare non-parametric data. RESULTS: Both groups showed significant improvements with similar gains in functional scores. Supination strength recovery was greater in the loop group (103% vs. 93%). There were no significant differences in range of motion or complications. Notably, only the anchor group showed cases of Popeye deformity (2 patients). Return to sport was achieved in 95.3% of patients, typically within 3 months. No revision or implant-related issues occurred. CONCLUSION: Loop tenodesis offers equivalent clinical outcomes to anchor-based techniques, with fewer aesthetic concerns and no implant-associated risks, making it a safe, reliable, and cost-effective alternative for biceps tenodesis.
25 May 2026
Read appraisal →BMJ open
Pilates during pregnancy for maternal musculoskeletal pain and discomfort: a systematic review and meta-analysis
OBJECTIVES: The predominant concern during pregnancy is musculoskeletal pain, often accompanied by additional discomforts such as anxiety and insomnia. Pilates, as a low-impact mind-body exercise, may be beneficial in managing musculoskeletal pain and associated symptoms in adults. This systematic review and meta-analysis aimed to synthesise the evidence on the effectiveness of Pilates for alleviating musculoskeletal pain and other discomforts during pregnancy. DESIGN: Systematic review and meta-analysis. DATA SOURCES: PubMed, Scopus, TRIP Medical Database, Web of Science and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched from inception to 14 January 2026. ELIGIBILITY CRITERIA: Randomised controlled trials and prospective non-randomised controlled studies that compared Pilates with other prenatal care modalities for improving musculoskeletal pain and discomfort were eligible. DATA EXTRACTION AND SYNTHESIS: Two researchers independently extracted data using standardised forms with subsequent cross-verification. Risk of bias was assessed using the Cochrane Risk of Bias V.2.0 tool. Meta-analyses were performed using random-effects models. The certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development and Evaluation approach. RESULTS: 13 studies were included in the systematic review, of which 10 contributed data to the meta-analysis. Primary outcomes were pain intensity and disability level. Pain intensity was assessed using Visual Analogue Scale (range 0-10). Moderate-certainty evidence demonstrated that Pilates significantly reduced musculoskeletal pain during pregnancy (mean differences (MD)=-2.59, 95% CI -4.19 to -1.00; I²=91%, p=0.001). Low-certainty evidence suggested that Pilates reduced pregnancy-related disability (standardised mean differences (SMD)=-1.82, 95% CI -2.99 to -0.66; I² = 93%, p=0.002). Secondary outcomes comprised sleep quality and psychological status. Multivariate meta-analysis showed significant improvements in psychological status (SMD=-0.86, 95% CI -1.31 to -0.42; τ²=0.4, I²=86%, p=0.0005) but did not yield statistically significant improvements in sleep quality (MD=-1.93, 95% CI -4.70 to 0.84; I²=93%, p=0.17). Both secondary outcomes were supported by very low-certainty evidence. Risk of bias assessment rated three studies as high risk, three as unclear risk and seven as low risk. Formal assessment of publication bias was not feasible owing to the limited number of studies (fewer than 10 per outcome). CONCLUSION: This meta-analysis demonstrates that, compared with standard care, structured prenatal Pilates significantly alleviates musculoskeletal pain and disability while also enhancing psychological status. PROSPERO REGISTRATION NUMBER: CRD42024628027.
14 May 2026
Read appraisal →BMC health services research
Barriers and facilitators to telerehabilitation implementation: a mixed-methods study of German physiotherapists
BACKGROUND: The COVID-19 pandemic accelerated telehealth service adoption in physiotherapy, including telerehabilitation (TR). However, the extent of TR use and factors influencing its implementation in Germany remain unclear. This study aimed to evaluate TR use in physiotherapy (during COVID-19 lockdowns, current use, future intentions, conditions treated, content, setting, and session type) and identify barriers and facilitators from physiotherapists' (PTs) perspectives. METHODS: A mixed-methods sequential explanatory design was employed, combining a cross-sectional online survey (n = 152) with two focus group interviews (n = 9). The survey collected data on demographics, TR use, barriers, and facilitators. Focus groups explored themes emerging from the survey in greater depth. Data were analyzed using descriptive statistics and content analysis, integrating quantitative and qualitative findings. Quantitative and qualitative findings were integrated through explanatory linking and joint display in a comparative analysis table. RESULTS: TR use peaked during COVID-19 lockdown (32.26%) but decreased to 18.06% by October 2022, with 26.45% intending future use and 43.87% considering it. Among TR users, musculoskeletal conditions were most commonly treated (75%), followed by sports (38%), pulmonology (33%), and neurology (27%). The primary barrier was lack of physical examination (74% agreement). While technical challenges were not reported as a major barrier in the survey, interviews revealed significant concerns about insufficient internet bandwidth and technical infrastructure. Common reasons for using TR included promoting patient self-management (78% agreement) and broadening therapy options (69% agreement). Qualitative data identified additional implementation facilitators, including structured implementation processes, appropriate technical infrastructure, and patient involvement in decision-making. CONCLUSION: While TR implementation in German physiotherapy shows growth potential, several barriers currently limit its adoption. Successful implementation requires addressing PTs capabilities, knowledge gaps, professional identity concerns, and environmental factors. Addressing these issues could enhance patient care quality, increase service accessibility, and advance healthcare delivery models.
13 May 2026
Read appraisal →AJNR. American journal of neuroradiology
High-Resolution 2D versus 3D Lumbar Spine MRI Optimized with a Deep Learning Reconstruction Algorithm and Prototype Conformal Coil
BACKGROUND AND PURPOSE: Lumbar spine MRI is predominantly performed using 2D FSE sequences. 3D FSE sequences offer potential advantages over 2D, especially with recent advances in deep-learning reconstruction (DLR) and the use of conformal high-density coil arrays to improve the SNR. This study aimed to compare image quality and diagnostically relevant differences between 2D and 3D lumbar spine protocols, both using a prototype conformal coil and DLR. MATERIALS AND METHODS: Subjects with lumbar curvature referred for routine lumbar spine MRI were prospectively recruited (n=31). A prototype, conformal spine coil array with 70 total posterior receiver elements was used at 3T. DLR was applied to both the 2D- and 3D-protocols to improve sharpness and reduce noise. For 3D, a prototype DLR ("Sonic DL") technique was applied to provide higher acceleration factors. Three attending musculoskeletal radiologists assessed 2D and 3D T1-weighted (T1), T2-weighted (T2), and STIR sequences for image quality and diagnostic performance. The Gwet agreement coefficient (AC1, AC2) or intraclass correlation (ICC) was used to assess interrater agreement, while the Wilcoxon signed rank test or a sign test with Bonferroni correction assessed differences between 2D and 3D sequences. RESULTS: There was an overall 14% scan time reduction of 3D compared with 2D sequences, and a 38% time reduction for T2 sequences. All 3D sequences exhibited similar image quality to 2D. 3D T2 sequences had less CSF flow artifact than 2D T2 sequences. Interrater agreement was mostly comparable between all 2D and 3D assessments, including excellent agreement for the spinal canal area (ICC = 0.952 [95% CI: 0.907-0.976] for 2D, 0.941 [0.866-0.973] for 3D), good agreement for foraminal stenosis assessments on T2 sequences (AC2 = 0.713 to 0.795 for 2D, 0.706-0.832 for 3D), and good agreement for fracture detection on T1 sequences (AC1 = 0.869 for 2D, 0.815 for 3D). CONCLUSIONS: DLR-enabled 3D lumbar spine MRI offers comparable image quality to 2D with reduced overall scan time.
11 May 2026
Read appraisal →Joint diseases and related surgery
High diagnostic accuracy of a resnet50-based deep learning model for osteochondral lesions of the talus on magnetic resonance imaging
OBJECTIVES: This study aims to evaluate the diagnostic performance of a ResNet50-based convolutional neural network (CNN) in detecting osteochondral lesions of the talus (OLTs) on magnetic resonance imaging (MRI) and to compare its efficacy between T1- and T2- weighted sequences. MATERIALS AND METHODS: A total of 219 ankle MRI scans were reviewed retrospectively, including 60 with confirmed OLTs and 159 without lesions. From each study, coronal and sagittal T1- and T2-weighted images were extracted and standardized to 224 × 224 pixels. Augmentation techniques were applied to strengthen model training. Data were divided into training, validation, and test sets in a 60:20:20 split. A ResNet50 model initialized with ImageNet weights was fine-tuned using crossentropy loss with class weighting. Diagnostic performance was summarized with accuracy, precision, recall, and F1-scores. RESULTS: The model performed better on T1 sequences, achieving an accuracy of 94.1% (95% confidence interval [CI] 88.3-97.1%) and an area under the curve [AUC] of 0.93 (95% CI 0.87-0.97), with patient cases classified at 0.92 precision and 0.82 recall. Healthy controls in the T1 group were recognized with 0.95 precision and 0.98 recall. In contrast, T2 sequences were less reliable, showing an accuracy of 87.2% (95% CI 80.5-91.9%) and an AUC of 0.91 (95% CI 0.85-0.95). Precision for patient cases in the T2 group was notably lower (0.65) despite a recall of 0.81. Misclassifications were more frequent in the T2 dataset, as evidenced by the confusion matrices. CONCLUSION: Even with a relatively modest dataset, the ResNet50 model delivered strong results for T1-weighted MRI. While T2 images proved more challenging, suggesting that deep learning can add value to routine assessment of OLTs.
5 May 2026
Read appraisal →Journal of medical imaging and radiation sciences
Automatic field-of-view planning for magnetic resonance shoulder imaging using Deep Learning
INTRODUCTION: Accurate prescription of oblique coronal and oblique sagittal field of views (FOV) is essential for diagnostic shoulder MRI. Manual planning is radiographer-dependent, time-consuming, and subject to inter- and intra-operator variability, leading to inconsistent image quality and incomplete coverage. Although deep learning (DL) has advanced automated scan planning in non-oblique planes, oblique shoulder prescriptions remain underexplored; an automated DL approach could standardize FOV prescription, reduce operator dependence, and improve reproducibility and workflow without compromising diagnostic quality. METHODS: In this retrospective multicenter study, 575 shoulder MRI examinations (2019-2025) from four sites were included. Sites A (n=151) and B (n=220) were used for training; testing was performed on sites C (n=61), and D (n=143). A two-stage pipeline was implemented using five oriented bounding box (OBB) variants of YOLOv11 (n, s, m, l, x): Stage 1 performed slice selection; Stage 2 performed FOV prescription. Performance was evaluated against radiographers' prescriptions using mean absolute slice difference (MASD, slices), intersection over union (IoU), and mean absolute angle difference (MAAD, degrees). Clinical utility was assessed by three raters. RESULTS: The YOLOv11-OBB-l model achieved the lowest MASD for Stage 1 (1.016±0.153 slices). For Stage 2, YOLOv11-OBB-x performed best (coronal IoU, 0.847±0.003; sagittal IoU, 0.852±0.007; MAAD, 3.259±0.190°). During testing across each site, MASD ranged from 0.700±0.837 to 1.192±2.550 slices; MAAD from 2.811±2.348 to 4.396±7.158°; coronal IoU from 0.800±0.092 to 0.872±0.065; and sagittal IoU from 0.824±0.111 to 0.887±0.047. Mean clinical utility was 97.2%. Performance was noninferior to interrater variability across all sites and metrics. CONCLUSION: DL-based automated FOV prescription for shoulder MRI achieves performance comparable to radiographers, generalizes across institutions, and demonstrates high clinical utility.
4 May 2026
Read appraisal →Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Synthetic computed tomography from magnetic resonance imaging: An editorial on deep learning approaches for hip and knee image translation.
This editorial provides a concise overview of synthetic computed tomography (CT) generation from magnetic resonance imaging (MRI) for musculoskeletal care, with a focus on hip and knee applications. We outline why interest in this area is growing, including the potential to reduce radiation exposure, consolidate imaging into a single visit, and align measurements across modalities for preoperative planning and longitudinal follow-up. At a high level, we summarize how deep learning methods transform MRI into CT-like volumes and what that could mean for bone morphology, implant planning, and combined assessment of bone and soft tissue. We briefly introduce representative model classes, including conditional generative adversarial networks that pair realism objectives with voxelwise constraints and diffusion models that replace adversarial training with iterative denoising for stable optimization and strong anatomical fidelity. Our goal is to orient clinicians, engineers, and researchers to the key promises and open questions, and to motivate collaborative studies that test whether synthetic CT can deliver reliable, reproducible, and clinically useful information at the point of care.
4 May 2026
Read appraisal →Medical science monitor : international medical journal of experimental and clinical research
Effects of Abdominal Bracing Versus Abdominal Hollowing on Abdominal Muscle Thickness and Activation During Static and Dynamic Trunk Stabilization Tasks in Healthy Adults
BACKGROUND Abdominal hollowing selectively targets deep abdominal muscles, whereas abdominal bracing emphasizes global activation of the superficial musculature. This study aimed to compare the effects of abdominal bracing and abdominal hollowing on abdominal muscle thickness and activation during trunk stabilization tasks in healthy adults. MATERIAL AND METHODS Eighteen healthy adults (9 males, 9 females) participated in a randomized crossover study. Abdominal bracing and hollowing were performed in random order with a 1-week wash-out period. Muscle thickness of the rectus abdominis (RA), external oblique (EO), internal oblique (IO), and transversus abdominis (TrA) were measured using ultrasound imaging. Surface electromyography assessed activation of the RA, EO, and IO during a static hook-lying position and a dynamic supine toe-tap task. RESULTS RA thickness was greater during bracing than hollowing (10.07±1.55 mm vs 9.22±1.46 mm, p=0.032), whereas IO and TrA thicknesses were greater during hollowing (IO: 8.65±3.18 mm vs 6.63±2.14 mm; TrA: 4.08±1.23 mm vs 2.41±0.80 mm, both p<0.05), representing an approximately 31% increase in TrA thickness. During the static task, activation of the RA, EO, and IO was higher during hollowing (p<0.05), whereas no significant differences were observed during the dynamic task. CONCLUSIONS Abdominal hollowing preferentially enhances deep muscle activation during low-load static conditions, whereas abdominal bracing promotes global muscular engagement. With increasing task demands, stabilization strategies converge, indicating task-dependent neuromuscular coordination.
18 Apr 2026
Read appraisal →Medicine
Bibliometric and visual analysis of artificial intelligence-related research in osteoarthritis: Trends and frontiers (2008-2025)
Osteoarthritis (OA) causes joint pain, functional impairment, and significantly impacts patients' quality of life. Recent advancements in artificial intelligence (AI) technology have provided new opportunities for OA diagnosis, treatment, and management. This study aims to understand the knowledge structure and research frontiers of "AI and OA" using bibliometric methods. Literature on "AI and OA" from January 1, 2008, to March 31, 2025, was retrieved from the Web of Science Core Collection database. VOSviewer, CiteSpace, and the R package "bibliometrix" were used for analysis. The study included 659 articles from 59 countries. Since 2019, the number of scientific outputs related to OA and AI has increased annually. The United States, China, and the United Kingdom are the main contributing countries, with Harvard University, Seoul National University, and the University of California system as leading institutions. The "JOURNAL OF ARTHROPLASTY" and "OSTEOARTHRITIS AND CARTILAGE" are the most contributing journals. Among the 2404 authors, GUERMAZI A and ROEMER FW are the most influential. Thematic trend analysis shows that AI and machine learning are driving new trends in OA research, especially in diagnosis, treatment planning, and patient monitoring. This paper summarizes the bibliometric trends and development in "AI and OA" studies over 18 years, identifying the latest research frontiers and hot topics. It provides valuable references for related research and highlights the importance of interdisciplinary collaboration as research topics diversify.
12 Apr 2026
Read appraisal →PloS one
Effects of scapular-focused movement-based exercises on sports performance of athletes with scapular dyskinesis: A systematic review
BACKGROUND: Scapular dyskinesis is a common dysfunction among athletes, particularly in overhead sports, leading to pain, reduced range of motion (ROM), and impaired performance. Movement-based exercises are increasingly used to address these issues, but their overall impact on sports performance remains unclear. OBJECTIVE: This systematic review aims to evaluate the effects of movement-based exercises on sports performance in athletes with scapular dyskinesis. METHOD: A comprehensive search was conducted in Web of Science, Scopus, and PubMed up to July 30, 2025, following PRISMA guidelines. Data were extracted and assessed for risk of bias using RoB-2 and ROBINS-I tools. A narrative synthesis was performed due to study heterogeneity. RESULTS: Fourteen studies (8 RCTs and 6 non-RCTs) involving 412 participants with a mean age of 23.8 years assessed movement-based interventions lasting from a single session to 24 weeks, primarily focusing on scapular stabilization, kinetic chain control, and proprioception over 6-8 weeks with around three sessions per week. Moderate-certainty evidence suggests that exercise likely improves shoulder function, disability, and glenohumeral range of motion over 6-12 weeks. However, the evidence for pain reduction and improvement in rotator cuff/scapular strength is of low certainty, showing mixed effects depending on the specific program. Evidence for improvement in scapular kinematics is also of low certainty. Sport-specific performance outcomes, such as throwing velocity, remain highly uncertain due to small sample sizes and conflicting results from RCTs. CONCLUSION: Movement-based exercises may be considered by athletes with scapular dyskinesis to potentially improve shoulder function and glenohumeral range of motion; however, the certainty of evidence for effects on pain relief, strength, and sports performance is very low. Therefore, strong recommendations cannot be made at this stage. More tailored programs and well-structured RCTs are needed to clarify these effects.
11 Apr 2026
Read appraisal →PloS one
Voxel-wise deep learning segmentation of hydroxyapatite and iodine in spectral photon-counting CT: A quantitative phantom study
Accurate non-invasive identification of hydroxyapatite (HA) deposits is important for diagnosing calcific musculoskeletal disease and quantifying vascular calcification, but conventional and dual-energy CT often struggle to distinguish HA from iodinated contrast because of overlapping attenuation, noise, and beam-hardening artifacts. Spectral photon-counting CT (SPCCT) offers improved energy resolution and spatial fidelity, yet most deep-learning approaches in spectral CT focus on continuous density regression or anatomical segmentation rather than direct voxel-wise material labeling. We developed SPFF-UNet, a spectral-preserving 3D segmentation model for direct classification of HA and iodine concentrations from five-bin SPCCT volumes without material-decomposition preprocessing. A cylindrical phantom containing twelve materials was scanned at 0.1 mm isotropic resolution, including five HA concentrations, three iodine concentrations, three soft-tissue equivalents, and water. SPFF-UNet integrates spectral squeeze-excitation, EnergyFiLM, and FourierGate to preserve and exploit multi-energy information throughout the network. The model was trained for thirteen-class voxel-wise segmentation and compared with five established 3D architectures under matched training conditions. SPFF-UNet achieved the best macro-averaged performance on a held-out phantom scan (Dice 0.72 ± 0.01, IoU 0.59 ± 0.01, sensitivity 0.73 ± 0.01, precision 0.71 ± 0.01), outperforming the strongest comparator, ResUNet++ (Dice 0.66 ± 0.02, IoU 0.46 ± 0.02, sensitivity 0.67 ± 0.02, precision 0.61 ± 0.03). Performance gains were concentrated in mid/low-contrast HA and low-concentration iodine, with reduced slice-wise variability and fewer HA-iodine misclassifications. These results suggest that preserving spectral information and applying targeted spectral modulation can improve concentration-aware voxel classification from SPCCT. This phantom-based proof-of-concept provides a basis for future in vivo validation.
11 Apr 2026
Read appraisal →JMIR research protocols
Targeted Real-Time Assessment of Chronic Pain (TRAC-Pain) in Youth: Protocol for a Digital Biosignature Development Through a Prospective Observational Cohort Study
BACKGROUND: Approximately 1 in 5 children and adolescents live with chronic pain, with musculoskeletal (MSK) pain being one of the most prevalent subtypes. Unfortunately, some studies show that less than half of youth (<18 years) experience improvements with existing evidence-based treatments. Self-report measures-the current gold standard for monitoring the pain experience-are limited in their use as a single point-of-care assessment and their vulnerability to recall bias. The ubiquitous adoption of wearable technology presents a promising solution for improved monitoring of the pain experience via real-time tracking through a multisystemic lens. OBJECTIVE: The purpose of this study is (1) to develop a digital biosignature of the pain experience in youth with chronic MSK pain and (2) to assess the feasibility, acceptability, and appropriateness of the captured data for future applications. METHODS: All aspects of this study were designed in partnership with people with lived experience and patient advocacy partners. This is a longitudinal observational cohort study, with all study activities taking place remotely over a 12-week period. Up to 500 youth (between 14 and 24 years old) with chronic MSK pain will be enrolled through a multipronged recruitment strategy to ensure a representative sample. The participants will wear an Apple Watch throughout the study to continuously monitor physiological, sleep, and physical activity metrics. In addition, participants will complete brief "daily check-in" surveys that include gold-standard measures of the pain experience (eg, pain interference, mood, fatigue) and the option to report a "pain flare" (ie, a temporary but noticeable worsening of the usual pain symptoms). Participants will also complete a modified online Trier Social Stress Test and a 30-second sit-to-stand task to capture individual responses to standardized challenges. Traditional machine learning and deep representation learning methods will be used to develop a digital biosignature of the pain experience. The accuracy of the biosignature will be assessed through measures of model performance as compared to gold-standard self-reports. RESULTS: This study was funded in September 2024, with data collection beginning in March 2025. As of December 15, 2025, 190 participants are enrolled, with data collection and analysis ongoing. CONCLUSIONS: This is the first study to leverage wearable health technology for real-time monitoring of the pain experience in youth with chronic MSK pain. The resulting digital end points are expected to heighten the rigor of clinical trials and provide opportunities for individually tailored interventions. The second phase of this study will investigate the implementation of "wellness alerts" triggered by abnormal smartwatch readings. Alerts would empower users toward preemptive self-management strategies, thereby enhancing self-efficacy in those living with chronic MSK pain. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/84781.
8 Apr 2026
Read appraisal →CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne
Behavioural nudges to reduce low-value care for low back pain in the emergency department (NUDG-ED): a 2 × 2 factorial, pragmatic cluster randomized trial
BACKGROUND: Low-value care exposes patients to unnecessary risk and wastes scarce health resources. We aimed to determine if patient- or clinician-directed nudges could reduce low-value care for low back pain in the emergency department. METHODS: We conducted a 2 × 2 factorial, cluster randomized controlled trial involving patients with low back pain presenting to emergency departments. Eight emergency departments were randomized to receive patient nudges (6 electronic information posters discouraging unnecessary imaging and opioids, displayed on 55-inch screens in waiting rooms), clinician nudges (3 electronic health record alerts that provided indications for lumbar spine imaging and suggested alternatives to opioids), both patient and clinician nudges, or no nudges. The primary outcome was the proportion of encounters for low back pain with low-value care, defined as non-indicated lumbar spine imaging test, opioid prescription at discharge, or both. We calculated odds ratios (ORs), adjusted for baseline and clustering. RESULTS: There were 3770 encounters for low back pain during the study period. The overall baseline prevalence of low-value care was 41.6%. During the intervention period, the proportion of encounters with low-value care reduced to 36.4% with patient nudges versus 38.1% without, but the difference was not significant (adjusted OR 0.80, 95% confidence interval [CI] 0.51 to 1.27). The proportion of encounters with low-value care was 39.4% with clinician nudges versus 35.0% without (adjusted OR 1.31, 95% CI 0.84 to 2.05). We did not observe an interaction effect between the interventions (p = 0.4). The patient nudge may have reduced strong beliefs among patients in the value of imaging for low back pain. We found no important differences in secondary outcomes. INTERPRETATION: Nudges - including waiting room information posters targeting patients and electronic health record alerts targeting clinicians - did not reduce low-value care in emergency departments. TRIAL REGISTRATION: www.anzctr.org.au, ACTRN12623001000695.
8 Apr 2026
Read appraisal →PloS one
Baseline associations between thoracic expansion, respiratory function, and pain in nonspecific low back pain: A cross-sectional analysis from the DIAFRAGMA trial
OBJECTIVE: To examine the associations of respiratory variables (chest and abdominal expansion, spirometry) with pain, lumbar mobility, and quality of life in nonspecific low back pain (NSLBP) using baseline data from the DIAFRAGMA trial. DESIGN: Cross-sectional study using baseline data from a randomized controlled trial. PARTICIPANTS: Fifty subjects (22 females [44%], 28 males [56%]; mean age 45.96 years; BMI 26.97 kg/m²) with NSLBP recruited between January and July 2024. Main measures: Axillary, sternal, and abdominal inspiratory expansion; spirometric parameters (FVC, FEV1); pain via pressure pain thresholds (PPT), Schober test, FFT; and quality of life via SF-12. METHODS: Pearson correlations assessed relationships between respiratory and clinical variables. Multiple linear regression adjusted for age, sex, and BMI. Significance was set at p < 0.05 with Bonferroni correction. RESULTS: Abdominal expansion correlated with FVC (r = 0.45; p < 0.001), FEV1 (r = 0.39; p = 0.003), PEF (r = 0.28; p = 0.03), axillary (r = 0.47; p < 0.001) and sternal expansion (r = 0.58; p < 0.001), PPT at L4 (r = 0.26; p = 0.04) and L5 (r = 0.29; p = 0.02), and SF-12 (r = 0.25; p = 0.04). BMI correlated negatively (r = -0.31; p = 0.02). In regression, abdominal expansion independently predicted pain after adjustment. CONCLUSIONS: Reduced thoracoabdominal expansion and spirometric performance are associated with greater pain and poorer quality of life in NSLBP, supporting the diaphragm's role in low back pain.
5 Apr 2026
Read appraisal →European journal of orthopaedic surgery & traumatology : orthopedie traumatologie
Radiological and surgical outcomes after graduation to posterior spinal fusion surgery following magnetically controlled growing rod treatment for early-onset scoliosis: a systematic review and single-arm meta-analysis
PURPOSE: Early-onset scoliosis (EOS) is a challenging condition for spinal surgeons. Growing rods represent the standard treatment for patients requiring surgery, and magnetically controlled growing rods (MCGR) are increasingly adopted. Many patients ultimately transition to posterior spinal fusion (PSF), although the necessity of definitive fusion remains debated. This systematic review and single-arm meta-analysis aimed to evaluate radiographic and surgical outcomes of PSF following MCGR treatment. METHODS: A systematic literature search was conducted in PubMed, Google Scholar, Medline, Scopus, Embase, and the Cochrane Library up to April 2025, following PRISMA guidelines. Eligible studies were identified using a PIO (Population, Intervention, Outcome) framework. Extracted data included demographic, radiographic, and surgical outcomes, including complications. A single-arm meta-analysis compared pre- and post-PSF radiographic parameters (main curve magnitude, thoracic kyphosis (TK), T1-T12 and T1-S1 lengths). Risk of bias was assessed using the MINORS tool. RESULTS: Eight retrospective studies were included (370 patients), of which six were eligible for quantitative synthesis. The meta-analysis demonstrated significant improvements in all radiographic parameters (main curve magnitude, TK, T1-T12 and T1-S1 lengths, p < 0.05). The pooled overall postoperative complication rate after PSF was 17.2%. CONCLUSIONS: PSF appears to be a viable treatment option following MCGR, providing significant deformity correction with a relatively acceptable complication rate. Nevertheless, the current evidence is limited by small, retrospective cohorts. Larger, prospective studies with standardized reporting of surgical variables and clinical outcomes are needed to better define the role of final fusion and to optimize treatment protocols.
4 Apr 2026
Read appraisal →European radiology
Large language model-based uncertainty-adjusted label extraction for artificial intelligence model development in upper extremity radiography
OBJECTIVES: To evaluate GPT-4o's zero-shot ability to extract structured diagnostic labels (with uncertainty) from free-text radiology reports and to test how these labels affect multi-label image classification of musculoskeletal radiographs. MATERIALS AND METHODS: This retrospective study included radiography series of the clavicle (n = 1170), elbow (n = 3755), and thumb (n = 1978). After anonymization, GPT-4o filled out structured templates by indicating imaging findings as present ("true"), absent ("false"), or "uncertain." To assess the impact of label uncertainty, "uncertain" labels of the training and validation sets were automatically reassigned to "true" (inclusive) or "false" (exclusive). Label-image pairs were used for multi-label classification using the ResNet50 architecture. Label extraction accuracy was manually verified on internal (clavicle: n = 233, elbow: n = 745, thumb: n = 393) and external test sets (n = 300 for each). Performance was assessed using macro-averaged receiver operating characteristic (ROC) area under the curve (AUC), precision, recall curves, sensitivity, specificity, and accuracy. AUCs were compared with the DeLong test. RESULTS: Automatic extraction was correct in 98.6% (60,618 of 61,488) of labels in the test sets. Across anatomic regions, label-based model training yielded competitive performance measured by macro-averaged AUC values for inclusive (e.g., elbow: AUC = 0.80 (range, 0.62-0.87)) and exclusive models (elbow: AUC = 0.80 (range, 0.61-0.88)). Models generalized well on external datasets (elbow (inclusive): AUC = 0.79 (range, 0.61-0.87); elbow (exclusive): AUC = 0.79 (range, 0.63-0.89)). No significant differences were observed across labeling strategies or datasets (p ≥ 0.15). CONCLUSION: GPT-4o extracted labels from radiologic reports to train competitive multi-label classification models with high accuracy. Detected uncertainty in the radiologic reports did not influence the performance of these models. KEY POINTS: Question Can GPT-4o automatically extract high-accuracy, uncertainty-aware diagnostic labels from routine radiologic reports of the clavicle, elbow, and thumb for use in training multi-label image classifiers? Findings GPT-4o extracted labels with > 98% accuracy, and multi-label classifiers for clavicle, elbow, and thumb radiographs performed consistently regardless of how uncertainty was handled. Clinical relevance Automated GPT-4o-based labeling of routine clavicle, elbow, and thumb radiologic reports enables the rapid conversion of radiologic reports into structured multi-label training datasets, supporting scalable development of dedicated image classification models.
4 Apr 2026
Read appraisal →Ann Anat
Epiphyseal interfaces.
This work explores interconnected articular and growth plate osteochondral interfaces, via complementary anatomical, biomechanical and structural analyses that combine benchtop micro-computed tomography (microCT) and high-resolution synchrotron CT (sCT) imaging approaches. We show that bony sclerosis, well-documented in late-stage OA in humans, and the osteoarthritic STR/Ort mouse model, is preceded by the amplified regionalised expansion of articular calcified cartilage (ACC) where vulnerability load-induced failure is greatest. We further observed marked alterations in the number and size of resident osteocyte lacunae in pre-OA and OA regions in comparison to controls, which age healthily, underscoring the value of CT-based approaches for elucidating early OA-related changes. Notably, our findings imply that excessive ACC formation is an early consequence of disrupted endochondral ossification that precedes and potentially drives OA sclerosis, osteophytosis and indeed, articular cartilage lesions. By applying digital volume correlation (DVC) to sCT images of loaded murine knee joints under physiological conditions, we demonstrate that the joints from a healthy parental control strain of CBA mice efficiently dissipate compressive strain, whereas OA-prone STR/Ort exhibit high strain foci in the epiphyseal compartment prior to cartilage degeneration; suggesting that early strain accumulation may therefore promote early joint vulnerability and subsequently prime degenerative progression with age. Finally, we examined how the cartilaginous growth plate (GP), responsible for all longitudinal bone growth, contributes to mechanical epiphyseal stability for articulation. Analysis of mineralised trans-physeal GP structures (bridges), revealed their dynamic and sexually dimorphic character as well as their regulation by mechanical loading and osteotropic agents. DVC analyses of sCT-imaged murine knee joints subjected to identical physiologically oriented load, identified that regionally heterogeneous load-induced strain patterns in the GP are spatially synchronised to bridge location; a relationship substantiated by finite element simulation. Together, these findings establish intra-physeal GP bridges as regulatable and mechanically sensitive structures that coordinate the transfer of local GP strains, while enhancing epiphyseal stability by distributing compressive and shear strains to thereby permit cessation of longitudinal growth.
1 Apr 2026
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