Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
Simulation in healthcare : journal of the Society for Simulation in Healthcare
Future of Biometric Technology in Healthcare Simulation
Biometric technology shows substantial promise to enhance patient safety and transform provider training. In patient care, biometric technology enables outpatient providers to optimize chronic disease by alerting providers to changes in patient conditions with real-time data. In provider training, biometric technology can inform training environments and curriculum. Certainly, there are hurdles to technology implementation including hardware fragility, cost, and software integration. Despite challenges, biometric technology has been effectively implemented in simulations and real patient settings. Looking forward, future developments focused on data security and privacy, standardizing data collection, and improving system compatibility will facilitate the wider adoption of biometric technology and improve health care quality, especially in resource-limited settings. Collaborative efforts among informatics experts, patient safety advocates, and health care providers are essential to overcome challenges and fully leverage biometric technology's potential in enhancing patient outcomes. The purpose of this commentary is to review current applications, identify key challenges, and offer recommendations to the simulation community for testing biometric technology, use in training programs, and facilitating systems integration to improve outcomes.
2 Aug 2026
Read appraisal →Acta oncologica (Stockholm, Sweden)
User perceptions of machine learning models as decision support for colorectal cancer multidisciplinary team conferences (AID-SIM-2): a qualitative simulation study.
BACKGROUND: Multidisciplinary team (MDT) conferences are considered a cornerstone of decision-making in cancer diagnostics and care. However, the current literature has not demonstrated improved patient outcomes based on the decisions of the MDT conferences. AIM: We aimed to evaluate how four different decision-support modalities impacted the decision-making process and the internal discussions in the MDTs in a multicenter simulation study, with a focus on user perceptions. METHODS: Four colorectal cancer centers with MDTs participated. We performed four simulations in each center. Each simulation used a different decision-support tool: (1) Current standard, (2) Current standard plus a prediction model, (3) A structured data presentation tool, and (4) A structured data presentation tool plus the prediction model. Clinician- and model-estimated risks were compared, the treatment suggestions from each site were compared, questionnaires about user perceptions were conducted after Simulations 2, 3, and 4 using a Google Form link, and a semi-structured interview was conducted at each site after the last simulation. RESULTS: Similar distributions of risk groups between clinicians and models were found; however, distinct discrepancies in predictions arose, particularly with higher-risk patients, highlighting the need for standardization for more complex clinical cases. The primary perceived benefit of decision support was increased standardization of care, independent of the individual physicians' personal views. However, participants emphasized the necessity of clinician autonomy to overrule tool suggestions when identifying clinical nuances not captured by the model. CONCLUSIONS: The colorectal cancer MDTs expressed a positive view regarding the use of prediction models and other forms of decision-support in their workflow. While clinicians and prediction models had similar risk score distributions, they diverged in the assessment of specific individual patients.
1 Aug 2026
Read appraisal →Urolithiasis
A validated custom pipeline for three-dimensional kidney stone renderings to create an open access repository
Three-dimensional (3D) rendering of urologic pathology plays an important role in simulation-based education, surgical training, and computer vision research; however, a standardized, open-access repository of high-fidelity kidney stone models stratified by chemical composition is lacking. We developed and validated a reproducible photogrammetry-based pipeline to generate realistic 3D kidney stone renderings. Chemically characterized human stones composed of calcium oxalate monohydrate (COM) (n = 11), uric acid (UA) (n = 5), cystine (n = 4), magnesium ammonium phosphate hexahydrate/carbonate apatite (MAPH/CA) (n = 2), and calcium hydrogen phosphate dihydrate (CHPD) (n = 3) were photographed using a custom-built rotating stage and dual fixed 4 K cameras. Rendered models were sent to 25 endourologists using a 5-point Likert-scale survey assessing geometric and surface texture fidelity. Successful 3D renderings were obtained for 8/11 COM stones, 5/5 UA stones, 2/2 MAPH/CA fragments, and 3/3 CHPD fragments, while all cystine stones failed to render. Across stone types, mean fidelity scores were highest for UA and COM stones (mean 3.8-3.9), intermediate for calcium phosphate stones (mean 3.6-3.8), and lowest for struvite stones (mean 3.0-3.3). Geometry scores were higher than texture scores overall, though this difference was not significant. Significant differences in geometric fidelity were observed across stone compositions (χ² = 9.30, p = 0.026). Inter-rater reliability was poor for individual evaluators (ICC = 0.10) but moderate for aggregated mean ratings (ICC = 0.67). This validated workflow enables the creation of generally realistic, open-access 3D kidney stone models (github.com/uro-glidar/3d-rendering-diverse-stones) for simulation, education, and future machine learning applications in endourology.
21 June 2026
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