High Adherence to System‐Level Performance Measures for Rheumatoid Arthritis in a National Early Arthritis Cohort Over Eight Years
Bibliographic record
Abstract
OBJECTIVE: To assess adherence to 3 system-level performance measures in a national early rheumatoid arthritis (RA) cohort. METHODS: Patients enrolled in the Canadian Early Arthritis Cohort (2007-2015) who met 1987 or 2010 American College of Rheumatology/European League Against Rheumatism criteria with <1 year of symptom duration and ≥1 year of followup after enrollment were included. Performance measures assessed were the percentage of RA patients seen in yearly followup, and the number of gaps between visits of >12 or >14 months, the percentage of RA patients treated with a disease-modifying antirheumatic drug (DMARD), and days from RA diagnosis to initiation of a DMARD. Results are shown stratified by enrollment year to assess for temporal changes in performance. RESULTS: A total of 1,763 early RA patients were included (mean age 54 years, 73% female, and 82% white). At enrollment, mean ± SD disease duration was 6 ± 3 months, and Disease Activity Score in 28 joints was 5.1 ± 1.5. Over 8 years, the proportion of patients seen in annual followup declined from 100% to 91%. Over followup, 42% of patients had 0 gaps in care of >12 months, and 64% had 0 gaps >14 months. The percentage of DMARD-treated early RA patients was and remained high (95-87%), and the percentage receiving DMARDs within 14 days of diagnosis was 75%. Median time-to-DMARD therapy was 1 day, indicating DMARDs were initiated at diagnosis (90th percentile 93 days). CONCLUSION: There was evidence of high adherence to system-level performance measures in this early RA cohort following a protocol. Small declines in performance were noted with increasing length of patient followup. Our findings are useful for performance measure benchmarking.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".