Sex-Related Differences in Dispensation of Rheumatic Medications in Older Patients With Inflammatory Arthritis: A Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: The aim of our study was to compare dispensation of rheumatic medications between older male and female patients with early rheumatoid arthritis (RA) and psoriatic arthritis (PsA). METHODS: This retrospective cohort study was performed using health administrative data from Ontario, Canada (years 2010-2017), on patients with incident RA and PsA, who were aged ≥ 66 years at the time of diagnosis. Yearly dispensation of rheumatic drugs was compared between older male and female patients for 3 years after diagnosis using multivariable regression models, after adjusting for confounders. The groups of drugs included in the analysis were disease-modifying antirheumatic drugs (DMARDs) classified as conventional synthetic DMARDs (csDMARDs) and advanced therapy (biologic DMARDs and targeted synthetic DMARDs), nonsteroidal antiinflammatory drugs (NSAIDs), opioids, and oral corticosteroids. Results were reported as odds ratios (ORs) with 95% CIs. RESULTS: We analyzed 13,613 patients (64% female) with RA and 1116 patients (57% female) with PsA. Female patients with RA were more likely to receive opioids (OR 1.39, 95% CI 1.22-1.58 to OR 1.51, 95% CI 1.32-1.72) and NSAIDs (OR 1.14, 95% CI 1.04-1.25 to OR 1.16, 95% CI 1.04-1.30). Dispensation of DMARDs showed no sex difference in either group. Subgroup analyses showed more intense use of advanced therapy in the RA cohort and of csDMARDs in the PsA cohort when patient and physician sex was concordant. CONCLUSION: This study did not identify any sex difference in the use of DMARDs among older patients with RA and PsA. The reasons for the higher use of opioids and NSAIDs among female patients with RA warrant further research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".