Primary Care Screening and Comorbidity Management in Rheumatoid Arthritis in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: Quality measurement for rheumatoid arthritis (RA) patients has largely focused on care provided by rheumatologists. Our aim was to develop and assess quality measures related to the screening and management of comorbidity in RA patients in primary care. METHODS: We used the primary care Electronic Medical Record Administrative data Linked Database in Ontario, Canada. We harmonized Canadian general population and RA clinical recommendations to develop and assess screening, process, and outcome measures. For each RA patient, 10 non-RA patients were matched by age and sex. Stratified analyses were performed, comparing patients with RA to those without RA, to assess the performance of quality measures. RESULTS: We compared 1,405 RA patients to 14,050 matched non-RA patients (72.8% female; mean age 62.5 years). Compared to non-RA patients, RA patients more frequently had influenza (44.9% versus 40.0%) and pneumococcal (40.4% versus 34.1%) vaccinations and bone mineral density testing (67.4% versus 58.1%). Herpes zoster vaccinations were less frequent among RA patients (13.8% versus 19.5%), as was screening for cervical cancer (58.6% versus 64.0%). No significant differences were observed between RA and non-RA patients in screenings for breast (70.7% versus 73.8%) or colorectal (31.7% versus 34.5%) cancers. Only a quarter of RA patients had a comprehensive cardiovascular risk assessment. No definitive differences were detected in the management of patients who had co-occurring cardiovascular disease or diabetes mellitus. CONCLUSION: For both RA and non-RA patients, compliance with Canadian recommendations for preventive medical services and screening for comorbid conditions in primary care was less than optimal. This indicates key targets for improvement.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".