Assessing Process of Care in Rheumatoid Arthritis at McGill University Hospitals
Bibliographic record
Abstract
OBJECTIVE: In rheumatoid arthritis (RA), quality indicators (QIs) are tools used to measure process of care. This study aimed to assess performance of selected QIs from the 2004 Arthritis Foundation's QI Set at 2 major sites of a university network of teaching hospitals. METHODS: The charts and electronic hospital records of 76 RA patients were audited to determine adherence to QIs. Logistic multivariate regression analyses were performed to investigate potential determinants of nonadherence and propose measures to facilitate better QI compliance, as a potential strategy towards RA care improvement. RESULTS: We identified consistent observance of QIs mandating prescription of disease-modifying antirheumatic drug therapy for all patients, drug adjustment with disease activity, prednisone tapering, and bisphosphonate therapy if indicated for patients on glucocorticoids. However, there was either lack of documentation or true inconsistent adherence to QIs dealing with radiograph performance, functional capacity assessment, and screening for hepatitis and tuberculosis before commencement of methotrexate and biologic agents, respectively. For the specific QIs analyzed, we did not find any definite independent associations with the studied variables. CONCLUSIONS: Our findings indicate that while there is frequent evidence for adherence to certain RA quality care standards at our centers, there is less compliance to others. Strategies to optimize the performance or documentation of those found most lacking, namely, functional capacity and screening for specific drug contraindications, could improve patient care. Radiographic disease monitoring, while lacking, may represent a move toward other more sensitive methods of RA progression detection, such as joint ultrasound. The inclusion of patient- and physician-derived information could help elucidate the reasons underlying nonadherence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".