System-level performance measures of access to rheumatology care: a population-based retrospective study of trends over time and the impact of regional rheumatologist supply in Ontario, Canada, 2002–2019
Bibliographic record
Abstract
OBJECTIVE: To determine whether there were improvements in rheumatology care for rheumatoid arthritis (RA) between 2002 and 2019 in Ontario, Canada, and to evaluate the impact of rheumatologist regional supply on access. METHODS: We conducted a population-based retrospective study of all individuals diagnosed with RA between January 1, 2002 and December 31, 2019. Performance measures evaluated were: (i) percentage of RA patients seen by a rheumatologist within one year of diagnosis; and (ii) percentage of individuals with RA aged 66 years and older (whose prescription drugs are publicly funded) dispensed a disease modifying anti-rheumatic drug (DMARD) within 30 days after initial rheumatologist visit. Logistic regression was used to assess whether performance improved over time and whether the improvements differed by rheumatology supply, dichotomized as < 1 rheumatologist per 75,000 adults versus ≥1 per 75,000. RESULTS: Among 112,494 incident RA patients, 84% saw a rheumatologist within one year: The percentage increased over time (adjusted odds ratio (OR) 2019 vs. 2002 = 1.43, p < 0.0001) and was consistently higher in regions with higher rheumatologist supply (OR = 1.73, 95% CI 1.67-1.80). Among seniors who were seen by a rheumatologist within 1 year of their diagnosis the likelihood of timely DMARD treatment was lower among individuals residing in regions with higher rheumatologist supply (OR = 0.90 95% CI 0.83-0.97). These trends persisted after adjusting for other covariates. CONCLUSION: While access to rheumatologists and treatment improved over time, shortcomings remain, particularly for DMARD use. Patients residing in regions with higher rheumatology supply were more likely to access care but less likely to receive timely treatment.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".