Effects of Use of Specialty Services on Disease-Modifying Antirheumatic Drug Use in the Treatment of Rheumatoid Arthritis in an Insured Elderly Population
Bibliographic record
Abstract
BACKGROUND: In community settings, disease-modifying antirheumatic drug (DMARD) use for rheumatoid arthritis (RA) falls short of treatment recommendations. This population-based study investigates the relationship between the use of DMARDs and specialty care in an insured population. METHOD: A cohort of individuals aged 65 or older with RA was identified from a population-based physician billing database in Ontario, Canada, together with information on visit rates to general and specialist physicians and visit-specific diagnoses. DMARD prescription data were obtained from the Ontario Drug Benefits Plan database. The proportions of individuals with RA using DMARDs and specialist care were calculated for the 43 counties in Ontario, and the relationship between the 2 was determined using logistic multilevel modeling, controlling for possible confounders. RESULTS: A total of 13,698 RA individuals aged 65 or older were identified, representing 1% of the 65-or-older population. Within this cohort, 58% received DMARDs and 68% made 1 or more RA-related visits to a specialist in 3 years. There was considerable variation by county in both the proportion of those with RA making visits to specialists (39-82 per 100 RA population) and receiving DMARDs (36-81%). The use of DMARDs was significantly associated with the use of specialist services by individuals with RA (odds ratio 1.9 [95% confidence interval 1.87, 1.88] for counties with highest versus lowest proportional use of specialists) independent of effects of age, sex, income, and comorbidities. CONCLUSION: Even in a universally funded setting, suboptimal treatment of RA is associated with lack of access to specialist services. These findings are likely applicable to many jurisdictions worldwide.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".