A Population‐Based Approach to Reporting System–Level Performance Measures for Rheumatoid Arthritis Care
Bibliographic record
Abstract
OBJECTIVE: To operationalize and report on nationally endorsed rheumatoid arthritis (RA) performance measures (PMs) using health administrative data for British Columbia (BC), Canada. METHODS: All patients with RA in BC ages ≥18 years were identified between January 1, 1997 and December 31, 2009 using health administrative data and followed until December 2014. PMs tested include: the percentage of incident patients with ≥1 rheumatologist visit within 365 days; the percentage of prevalent patients with ≥1 rheumatologist visit per year; the percentage of prevalent patients dispensed disease-modifying antirheumatic drug (DMARD) therapy; and time from RA diagnosis to DMARD therapy. Measures were reported on patients seen by rheumatologists, and in the total population. RESULTS: The cohort included 38,673 incident and 57,922 prevalent RA cases. The percentage of patients seen by a rheumatologist within 365 days increased over time (35% in 2000 to 65% in 2009), while the percentage of RA patients under the care of a rheumatologist seen yearly declined (79% in 2001 to 39% in 2014). The decline was due to decreasing visit rates with increasing follow-up time rather than calendar effect. The percentage of RA patients dispensed a DMARD was suboptimal over follow-up (37% in 2014) in the total population but higher (87%) in those under current rheumatologist care. The median time to DMARD in those seen by a rheumatologist improved from 49 days in 2000 to 23 days in 2009, with 34% receiving treatment within the 14-day benchmark. CONCLUSION: This study describes the operationalization and reporting of national PMs using administrative data and identifies gaps in care to further examine and address.
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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 | 0.060 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".