Gaps in care for rheumatoid arthritis: A population study
Bibliographic record
Abstract
OBJECTIVE: Treatment guidelines for rheumatoid arthritis (RA) now recommend early, aggressive, and persistent use of disease-modifying antirheumatic drugs (DMARDs) to prevent joint damage in all people with active inflammation, and evaluation by a rheumatologist, when possible. This research assesses whether care for RA, at a population level, is consistent with current treatment guidelines. METHODS: Using administrative billing data from the Ministry of Health in 1996-2000, all prevalent RA cases in British Columbia, Canada were identified. Data were obtained on all medications and all provincially-funded health care services. RESULTS: We identified 27,710 RA cases, yielding a prevalence rate of 0.76%, consistent with epidemiologic studies. DMARD use was inappropriately low. Only 43% of the entire RA cohort received a DMARD at least once over 5 years, and 35% over 2 years. When used, DMARDs were started in a timely fashion, but were not used consistently. Care by a rheumatologist increased DMARD use 31-fold. Yet, only 48% and 34% saw a rheumatologist over 5 and 2 years, respectively. DMARD use was significantly more frequent, persistent, and more often used as combination therapy with continuous rheumatologist care. DMARDs were used by 84% and 73%, 40%, and 10% of people followed by rheumatologists continuously and intermittently, internists, and family physicians, respectively (P < 0.001). NSAID use, physiotherapy, and orthopedic surgeries were similar across these 4 care groups. CONCLUSION: RA care in the British Columbia population was not consistent with current treatment guidelines. Efforts to educate family physicians and consumers about the shift in RA treatment paradigms and to improve access to rheumatologists are needed.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".