Understanding sex-related differences in healthcare utilisation among patients with inflammatory arthritis: a population-based study
Bibliographic record
Abstract
OBJECTIVES: Our aim was to compare patterns of musculoskeletal-related healthcare utilisation between male and female patients before and after the diagnosis of inflammatory arthritis (IA). METHODS: We used Ontario administrative health data to create three inception cohorts of adult patients with rheumatoid arthritis (RA), ankylosing spondylitis (AS) and psoriatic arthritis (PsA) diagnosed between April 2010 and March 2017. Healthcare utilisation indicators including visits to physicians, and use of musculoskeletal imaging and laboratory tests were assessed in each year for 3 years before and after diagnosis and compared between male and female patients using regression models adjusting for sociodemographic factors and comorbidities. Results were reported as ORs with 95% CIs for female patients compared with male patients. RESULTS: A total of 41 277 patients with RA (69% female), 8150 patients with AS (51% female) and 6446 patients with PsA (54% female) were analysed.Similar trends of sex-related differences were observed in all three cohorts. Before diagnosis, female patients were more likely to visit rheumatologists (OR 1.32-2.28) and family physicians (OR 1.03-1.15) for musculoskeletal reasons, whereas male patients were more likely to visit the emergency for musculoskeletal reasons (OR 0.76-0.87). A similar female predominance was observed regarding musculoskeletal imaging and laboratory tests before diagnosis. After diagnosis, female patients were more likely to remain in rheumatology care (OR 1.12-1.24). CONCLUSION: Female patients with IA have higher healthcare utilisation than male patients which may indicate biological differences in disease course or sociocultural differences in healthcare-seeking behaviour.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".