Care quality for rheumatoid arthritis patients in Quebec
Bibliographic record
Abstract
AIM: The aims of this study were to: (a) measure the proportion of CARTaGENE rheumatoid arthritis (RA) patients fulfilling pre-specified quality indicators (ie disease-modifying antirheumatic drug [DMARD] use, regular follow up, use of folate supplementation, use of vitamin D and calcium, exercise and smoking status); and (b) examine variation in DMARD use with respect to patient age, sex, education and income. METHODS: A cohort of RA patients was constructed based on the CARTaGENE survey and health administrative database. CARTaGENE is a large, established, population-based study which recruited 19 995 participants from four metropolitan regions in Quebec. Six quality indicators (QI) were assessed; four pertained to RA management and treatment received (use of DMARD therapy, annual medical visits, use of folate supplementation with methotrexate therapy, and use of calcium and vitamin D in steroid-exposed patients) and two pertained to lifestyle factors (physical activity and smoking cessation). QI were reported in terms of proportion of patients fulfilling them. Bayesian logistic regression analyses were preformed to investigate potential variation with DMARD use. RESULTS: Our cohort included 142 RA patients. The QI that pertain to RA pharmacotherapy and medical management ranged 60-80%. Regarding the QI focusing on lifestyle factors, 55% of patients reported performing moderate physical activity and only 16.6% reported current smoking. Results from the Bayesian logistic regression showed no definite associations between DMARD use and patient characteristics (age, education, income and sex). CONCLUSION: Our findings suggest a seemingly modest performance of Quebec's health-care system for RA patients, with respect to these QI.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".