Physician peer assessments for compliance with methadone maintenance treatment guidelines
Bibliographic record
Abstract
INTRODUCTION: Medical associations and licensing bodies face pressure to implement quality assurance programs, but evidence-based models are lacking. To improve the quality of methadone maintenance treatment (MMT), the College of Physicians and Surgeons of Ontario, Canada, conducts an innovative quality assurance program on the basis of peer assessments. Using data from this program, we assessed physician compliance with MMT guidelines and determined whether physician factors (e.g., training, years of practice), practice type, practice location, and/or caseload is associated with MMT guideline adherence. METHODS: Secondary analysis of methadone practice assessment data collected by the College of Physicians and Surgeons of Ontario, Canada. Assessment data from methadone prescribing physicians who completed their first year of methadone practice were analyzed. We calculated the mean percentage compliance per guideline per physician and global compliance across all guidelines per physician. Linear regression was used to assess factors associated with compliance. RESULTS: Data from 149 physician practices and 1,326 patient charts were analyzed. Compliance across all charts was greater than 90% for most areas of care. Compliance was less than 90% for take-home medication procedures; urine toxicology screening; screening for hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus (HIV), tuberculosis, other sexually transmitted infections, and completion of a psychosocial assessment. Mean global compliance across all charts and guidelines per physician was 94.3% (standard deviation = 7.4%) with a range of 70% to 100%. Linear regression analysis revealed that only year of medical school graduation was a significant predictor of physician compliance. DISCUSSION: This is the first report of MMT peer assessments in Canada. Compliance is high. Few countries conduct similar assessment processes; none report physician-level results. We cannot quantify the contribution of peer assessment, training, or self-selection to the compliance rates, but compared to other areas of practice these rates suggest that peer assessment may exert a significant effect on compliance. A similar assessment process may in other areas of clinical practice improve physician compliance.
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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 | 0.019 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".