Sex-specific risk profiles of drug-associated joint stiffness and deformity: a FAERS-based pharmacovigilance study with Canadian Vigilance Adverse Reaction Database validation
Bibliographic record
Abstract
OBJECTIVES: Joint stiffness and joint deformity are debilitating musculoskeletal adverse drug reactions, yet their risk profiles and sex-specific differences remain poorly characterised. This study aimed to identify drug-associated safety signals and sex disparities using real-world pharmacovigilance data. METHODS: Adverse event reports from the FDA Adverse Event Reporting System (FAERS; Q1 2004-Q2 2025) were analysed. Drug names and events were standardised using RxNorm and MedDRA 27.1. Disproportionality analyses (ROR) with false discovery rate correction were performed overall and by sex. External validation was conducted using the Canadian Vigilance Adverse Reaction Database (CVARDD). Weibull modelling assessed time-to-onset patterns. RESULTS: We identified 23,763 joint stiffness and 1,414 joint deformity reports, predominantly in females. For joint stiffness, frequently implicated drugs included methotrexate, dupilumab, and fluoroquinolones, alongside 20 newly detected signals (e.g. contrast media, ROR=217.1; gadopentetic acid, ROR=26.14). For joint deformity, alendronic acid, valproic acid, and somatropin showed strong associations, and nine novel signals were identified (e.g. vosoritide, ROR=125.78). Sex-stratified analyses revealed distinct risk profiles: females were more susceptible to bone metabolism and endocrine drugs, whereas males exhibited higher risks with enzyme replacement therapies. Time-to-onset analyses showed an early-failure pattern, with median onset as early as 63 days for methotrexate. Major signals were confirmed by the CVARDD. CONCLUSIONS: This study provides comprehensive real-world evidence of sex-specific differences in drug-associated joint stiffness and deformity, identifies 29 previously unreported signals, and highlights the early-onset nature of these adverse events. These findings support targeted monitoring and personalised risk management, and may inform future updates to drug safety labelling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".