Evaluating Quality of Care for Rheumatoid Arthritis for the Population of Alberta Using System-level Performance Measures
Bibliographic record
Abstract
OBJECTIVE: We evaluated 4 national rheumatoid arthritis (RA) system-level performance measures (PM) in Alberta, Canada. METHODS: Incident and prevalent RA cases ≥ 16 years of age since 2002 were identified using a validated case definition applied in provincial administrative data. Performance was ascertained through analysis of health data between fiscal years 2012/13-2015/16. Measures evaluated were as follows: proportion of incident RA cases with a rheumatologist visit within 1 year of first RA diagnosis code (PM1); proportion of prevalent RA patients who were dispensed a disease-modifying antirheumatic drug (DMARD) annually (PM2); time from first visit with an RA code to DMARD dispensation and proportion of incident cases where the 14-day benchmark for dispensation was met (PM3); and proportion of patients seen in annual follow-up (PM4). RESULTS: There were 31,566 prevalent and 2730 incident RA cases (2012/13). Over the analysis period, the proportion of patients seen by a rheumatologist within 1 year of onset (PM1) increased from 55% to 63%; however, the proportion of RA patients dispensed DMARD annually (PM2) remained low at 43%. While the median time to DMARD from first visit date in people who received DMARD improved over time from 39 days to 28 days, only 38-41% of patients received treatment within the 14-day benchmark (PM3). The percentage of patients seen in yearly follow-up (PM4) varied between 73-80%. CONCLUSION: The existing Alberta healthcare system for RA is suboptimal, indicating barriers to accessing specialty care and treatment. Our results inform quality improvement initiatives required within the province to meet national standards of care.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".