Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 10 appraisals
Journal of the American Medical Informatics Association : JAMIA
Disparate language and model effects on AI-based translation and recognition of genetic conditions.
INTRODUCTION: Artificial intelligence (AI) is increasingly prevalent. Patients and clinicians may use AI-based tools in many different languages. OBJECTIVE: To investigate AI translation tools for descriptions of genetic conditions and how AI identification of genetic conditions is affected by translations. MATERIALS AND METHODS: We used Neural machine translation (NMT) and large language-model (LLM) translation to translate descriptions of 40 genetic conditions into 191 and 93 languages, respectively. Excluding translations retaining English medical terms verbatim, we respectively focused on 139 and 70 languages. After assessing translations, we assessed the ability of 3 proprietary and 3 open-weight general LLMs to identify conditions in the translations. We analyzed how accuracy was affected by the conditions' prevalence in the literature, and attributes of the languages (the script, language family, and prevalence of the language in training sources). We also investigated adaptive translation for select languages. RESULTS: We found significant differences in condition identification based on the translation method, condition, language, and prediction model. The accuracy of some models was more affected than others by factors like the conditions' literature prevalence, language script, family, and language prevalence. Adaptive translation for select languages did not improve translations or diagnostic accuracy with the 3 tested LLMs. However, further analysis with 1 language showed that this approach was more effective with smaller LLMs. CONCLUSIONS: AI-based translation has variable performance, which can affect the ability of AI models to recognize genetic conditions. These findings should inform safe medical AI use to support consistent performance in different languages.
2 July 2026
Read appraisal →Medicine
Prevalence of GJB2 gene mutations in nonsyndromic hearing impairments: A systematic review and meta-analysis
BACKGROUND: Nonsyndromic hearing impairment (NSHI) is the most common type of inherited hearing loss. Mutations in GJB2, which encodes connexin 26, are a major cause worldwide, but their prevalence and variant distribution vary across populations. To systematically evaluate the global prevalence of GJB2 mutations and 5 hotspot variants (c.35delG, c.176_191del16, c.235delC, c.299_300delAT, and c.109G > A) in NSHI patients, and assess their diagnostic relevance. METHODS: A meta-analysis was conducted on studies published between January 2019 and June 2024 retrieved from PubMed, Scopus, Web of Science, and Cochrane Library. Only high-quality studies (Newcastle-Ottawa Scale ≥ 7) with molecular diagnostic data were included. Pooled prevalence, 95% confidence intervals, and heterogeneity (I2) were calculated. Subgroup and meta-regression analyses were performed according to geographic region. RESULTS: Fifty-one studies from 24 countries covering 18,968 patients with NSHI were included. The overall prevalence of GJB2 mutations was 25.9% (95% confidence intervals: 21.7-30.1%; I2 = 96%). The variant-specific rates included c.235delC (15.2%), c.35delG (12.0%), c.109G > A (7.6%), c.299_300delAT (3.1%), and c.176_191del16 (0.7%). The highest prevalence occurred in the Middle East (38.5%), whereas Africa had the lowest (11.1%). CONCLUSION: GJB2 mutations show marked geographic and ethnic variability in populations with NSHI. These findings support region-specific genetic screening to improve the early diagnosis, counseling, and targeted interventions for hereditary hearing loss.
31 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Genetic Creutzfeldt-Jakob disease associated with 5-octapeptide repeat insertion in the PRNP gene: case and pedigree report and literature review
BACKGROUND: Genetic prion diseases caused by octapeptide repeat insertions in the PRNP gene are rare. 5-octapeptide repeat insertion (5-OPRI) is an uncommon subtype that usually manifests as Creutzfeldt-Jakob disease (CJD) but shows marked variability in clinical phenotype and disease course. DISCUSSION: We report a patient with rapidly progressive gCJD caused by a 5-OPRI mutation. A systematic PubMed review identified genetically confirmed 5-OPRI cases with available clinical data. Demographic characteristics, clinical features, ancillary investigations, disease course, and PRNP codon 129 polymorphisms were analyzed. A total of 25 patients from 14 families were included. Age at onset ranged from 26 to 63 years (mean 44.8 ± 9.9 years). Disease duration ranged from 4 to 192 months. Dementia was present in all patients, and 84% showed frontotemporal dementia-like features. Cerebellar signs, extrapyramidal symptoms, and myoclonus occurred in 68%, 60%, and 44% of patients, respectively. Periodic sharp wave complexes were detected in 29.4% of EEGs, and cerebral atrophy was observed in all patients with neuroimaging. Most patients carried the MM genotype at PRNP codon 129 (83.3%). A short disease course was associated with more frequent akinetic mutism and myoclonus, while MM carriers had a younger age at onset than MV carriers. CONCLUSIONS: 5-OPRI-associated CJD exhibits marked heterogeneity in clinical and disease duration. The PRNP codon 129 polymorphism appears to influence disease onset and progression.
26 May 2026
Read appraisal →European journal of cancer (Oxford, England : 1990)
Invasive lobular and ductal breast cancers: a systematic review and metanalysis in association with BRCA1/2 mutation status
BACKGROUND: Invasive lobular breast carcinoma (ILC) differs from invasive ductal breast carcinoma (IDC) with respect to genetic alterations and risk factors. We aimed to quantify differences in the prevalence of germline BRCA1/2 mutations between these two patients' populations. MATERIALS AND METHODS: We conducted a systematic literature search to extract data on the association between BRCA1/2 mutation status and breast cancer (BC) histological subtype (ILC and IDC). Summary odds ratios (SOR) and 95% CI were calculated using random effects univariate and bivariate models and between-study heterogeneity investigated through meta-regression and subgroup analyses. RESULTS: Twelve studies, published between 1998 and 2025, were considered, including 8004 BC women. BRCA1 mutations were significantly less frequent in patients with ILC than IDC (SOR=0.32, 95%CI (0.16-0.65)) whereas no association was found overall for BRCA2 (SOR=1.28, 95%CI (0.95-1.72)). The difference between the association with BRCA1 and BRCA2 was statistically significant (p-value<0.001). There was no indication of publication bias in BRCA1 (Egger's and Begg's test p-value=0.23, 0.12). The between study heterogeneity was 29% in BRCA1 and 0% in BRCA2. Excluding papers with high risk of bias, BRCA2 mutations were more frequent in patients with ILC than in IDC (SOR=2.56, 95%CI (1.15-5.7)). This association was primarily driven by the bivariate random-effects model, which accounts for the correlation between BRCA1 and BRCA2 estimates. CONCLUSIONS: In studies with lower risk of bias, germline BRCA2 mutations were more frequent in patients with ILC than IDC. Conversely, BRCA1 mutations are more frequently observed in IDC, particularly among younger patients and those with a positive family history of BC.
17 May 2026
Read appraisal →PloS one
Analyzing the performance of deep learning splice prediction algorithms
SpliceAI is the leading tool for predicting splice-altering variants, but restrictive licensing limits clinical adoption. While open-source implementations have been published with author-reported comparisons, independent benchmarking across diverse datasets is needed to establish equivalence. We compared the original SpliceAI with two open-source implementations (OpenSpliceAI and CI-SpliceAI) and a legacy ensemble baseline across six datasets: a curated set of 1,316 validated variants, 213 variants with splice-assay data, 99,601 variants from the SPiP splicing prediction study, 242 manually curated deep intronic pathogenic variants, and two ClinVar-derived datasets comprising 53,600 intronic variants and 58,064 variants spanning all genomic contexts. The deep learning models were also evaluated against an ensemble of four legacy splice-prediction tools. Across all datasets, the deep learning algorithms outperformed the legacy ensemble. All three deep learning algorithms showed similar performance on the larger datasets dominated by canonical splice site variants (balanced accuracies 0.889-0.977). On the deep intronic benchmark, the original SpliceAI achieved the highest balanced accuracy (0.940), outperforming both CI-SpliceAI (0.890) and OpenSpliceAI (0.841). Critically, optimal thresholds for deep intronic variants were an order of magnitude lower than standard recommendations, indicating that default thresholds would miss the majority of pathogenic deep intronic variants. A correlation analysis showed that CI-SpliceAI maintained balanced concordance across event types, whereas OpenSpliceAI showed stronger correlation for loss events than gain events. Both implementations showed high positional agreement with SpliceAI, with exact splice-site match rates exceeding 90% across event types. Together, these results demonstrate that both open-source reimplementations of SpliceAI successfully reproduce the predictive behavior of the original algorithm across multiple evaluation contexts, while consistently outperforming traditional splice prediction methods. However, performance diverges on deeply intronic variants, and standard score thresholds are poorly calibrated for this variant class regardless of algorithm choice.
15 May 2026
Read appraisal →Journal of medical genetics
Clinically significant DNA variation from the GENCOV and HostSeq COVID-19 genome sequencing studies
BACKGROUND: Vast amounts of genome sequencing data generated from large-scale research studies like HostSeq provide an opportunity to summarise the spectrum of pathogenic variation in a subset of the Canadian population. Sharing variant-level data with public databases, like ClinVar, is crucial for advancing our understanding of genomic variants related to Mendelian diseases. However, such entries are often incomplete or contain discrepancies which render interpretation and classifications less useful. METHODS: The GENCOV and HostSeq cohorts were annotated and summarised using custom workflows to identify variants with a pathogenic and likely pathogenic (P/LP) classification in ClinVar. These variants were further filtered using custom gene panels, gnomAD frequency, variant type, gene-disease relationship and the number of reputable ClinVar laboratory submissions. Manually assessed variants from the GENCOV study were compared with ClinVar classifications to identify discrepancies. RESULTS: A total of 1956 unique variants were manually classified as P/LP through the GENCOV study. Of these, 65% (1276) were also identified in ClinVar, and among those, 69% (889) had concordant P/LP classifications. The manual assessment of unique exonic variants in GENCOV yielded a higher number of putative P/LP variants (1595), including truncating and missense variants compared with the 127 unique exonic P/LP variants in HostSeq. CONCLUSION: These results highlight that a large proportion of P/LP variation is either absent or has conflicting evidence for pathogenicity in ClinVar, emphasising the importance of periodic reassessment, discrepancy resolution and updates to ensure completeness and accuracy of public databases.
23 Apr 2026
Read appraisal →HGG advances
Gene-specific pathogenicity predictor for chromatin remodeling BAF complex-associated neurodevelopmental disorders
Advancements in whole-genome sequencing have increased the number of variants of uncertain significance (VUS) identified in human genomes. This has created a diagnostic bottleneck for genetic counselors tasked with sifting through these variants and determining those most likely to be causative for a patient's clinical presentation. Machine learning (ML) tools can aid in identifying pathogenic variants from VUS, but there is a need for gene-specific algorithms that predict pathogenic variants with high accuracy. To address this need, we present a workflow for developing gene-specific, ensemble-learning ML tools, that leverage outputs from other algorithms, locations of variants within the gene, and evolutionary conservation data to make a prediction of pathogenicity. Variants in SMARCA2 and SMARCA4 that are associated with rare neurodevelopmental diseases were used to screen 15 ML algorithms. A random forest learner was tuned to yield a final accuracy of 0.93 on holdout data. Generalizing this predictor to other BRG1/BRM-associated factor (BAF) complex proteins resulted in a sharp decline in performance. We trained a final predictor for all genes in the study to create a predictor that identifies pathogenic variants in these BAF subunits with an accuracy of 0.91 on holdout data. This predictor specific to BAF complex proteins performs with higher accuracy and area under the precision-recall curve than any other predictor. The decline in performance when generalized to other proteins emphasizes the need for the gene-specific calibration of predictors. Our workflow for the development of such models provides a quick, computationally inexpensive route for improving the ML tools available to genetic counselors.
12 Apr 2026
Read appraisal →Archives of gynecology and obstetrics
Misdiagnosis of different types of aneuploidy in preimplantation genetic testing (PGT): a systematic review and meta-analysis
PURPOSE: Recent studies have reported that embryos diagnosed as aneuploid by preimplantation genetic testing (PGT) can still result in successful live births after transfer. This suggests that, in addition to mosaic embryos, fully aneuploid embryos may also carry a risk of diagnostic error, potentially reducing the overall accuracy of PGT. Therefore, thoroughly investigating the risk and characteristics of aneuploidy misdiagnosis is crucial for optimizing PGT strategies and improving clinical outcomes in assisted reproductive technology (ART). METHODS: Relevant studies published from January 2000 to December 2024 were identified through PubMed and Web of Science. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). A diagnostic meta-analysis was conducted using a random-effects model to calculate pooled odds ratios (ORs) with 95% confidence intervals (CIs), combining sensitivity and specificity. Results were visualized using forest plots. RESULTS: A total of 22 studies were included to assess the discordance between trophectoderm (TE) biopsy and inner cell mass (ICM) or whole blastocyst (WB) results. The discordance rate for euploid embryo diagnosis was low (2.6%), whereas it was significantly higher for aneuploid embryos (9.2%). Segmental aneuploidies showed the highest discordance rate (17.4%). In the PGT-A population, misdiagnosis of segmental aneuploid embryos was particularly prominent (OR = 10.04, 95% CI: 7.60-13.27, I2 = 0%, P < 0.001). CONCLUSION: The results indicate that embryos with segmental aneuploidies have a significantly higher risk of misdiagnosis, especially in the PGT-A population. This highlights the need for caution when interpreting trophectoderm (TE) biopsy results involving segmental aneuploidies, to avoid misdiagnosis and the inadvertent discard of potentially viable embryos.
8 Apr 2026
Read appraisal →Genetics
Mapping whole-organism genetic comorbidities across model Species using unified ontologies
Understanding how genetic variation contributes to organism-wide phenotypes is critical for identifying mechanisms of disease. Here, we present a computational approach to analyze whole-organism comorbidities associated with genes underlying non-obstructive azoospermia (NOA), the most severe form of male infertility. We curated 204 mouse genes with experimentally validated spermatogenic failure and mapped their orthologs and phenotype annotations across humans, M. musculus, D. rerio, D. melanogaster, and C. elegans using a unified cross-species phenotype structure. This framework integrates a newly developed reproductive phenotype ontology with standardized whole-body phenotype categories to enable direct cross-species comparisons, using thousands of genotype-phenotype associations stored in model organism databases. Our analysis shows that most NOA genes have conserved orthologs across models, and that perturbation of these genes is frequently associated with non-reproductive phenotypes. In particular, integumentary defects and neoplastic phenotypes recur across species, supporting a shared genetic basis for epidemiological links between male infertility and systemic disease. Gene-level comorbidity patterns are partially predictable from single-cell RNA-seq, whole-body gene expression, and Gene Ontology annotations, suggesting that fundamental biological constraints shape the systemic consequences of reproductive gene dysfunction. Clustering genes by comorbidity profiles further distinguished genes with isolated reproductive effects from those with broad organismal consequences. This work demonstrates the power of ontology-based cross-species analysis for identifying pleiotropic effects of Mendelian disease mutations, and provides a resource, CoMorbidity DataBase Mapper (CoMo DBM), for joint analysis of genotype-phenotype associations in model organism databases.
6 Apr 2026
Read appraisal →Nat Med
Feasibility and clinical utility of expanded genomic newborn screening in the Early Check program
Although genomic sequencing presents groundbreaking newborn screening (NBS) opportunities, critical feasibility and utility questions remain. Here we present initial results from the Early Check program-an observational study assessing the feasibility and clinical utility of genomic NBS in North Carolina. Recruitment was statewide through mailed letters with electronic consent. Genome sequencing with analysis of 169 high actionability genes (plus 29 optional lower actionability genes) was performed using residual NBS dried blood spots. In 8 months, 1,979 newborns were screened, with 50 (2.5%) screen positives. Negative results were returned electronically, positive results by genetic counselors. Twenty-eight results (55%) were true positives, all received anticipatory guidance, surveillance and management recommendations, and referral to specialists as appropriate. We report technical feasibility and preliminary clinical utility finding, along with interpretation and follow-up challenges that hinder public health implementation. We propose standardized terminology to facilitate cross-study comparisons and accurate characterization of genomic NBS outcomes.
1 Nov 2025
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