Improved cardiovascular prevention using best CME practices: A randomized trial
Bibliographic record
Abstract
INTRODUCTION: It was hypothesized that after a continuing medical education (CME) event, practice enablers and reinforcers addressing main clinical barriers to preventive care would be more effective in improving general practitioners' (GPs) adherence to cardiovascular guidelines than a CME event only. METHODS: A cluster-randomized trial was conducted on a convenience sample of 122 GPs who were randomly assigned to either CME only (control group) or CME with practice enablers and reinforcers (PER group). In the PER group, nurses visited GPs' offices once a month to implement the clinical intervention on patients > or = 55 years old with a scheduled visit in the month following the nurse visit: (1) screening medical records for potentially undermanaged high-risk patients; (2) prompting physicians to reassess preventive care in these patients; (3) enclosing a checklist reporting most recent information relevant to guidelines' implementation; and (4) enclosing a summary of experts' recommendations in the form of a follow-up and treatment algorithm. RESULTS: A retrospective chart audit of 2344 consenting patients, potentially undermanaged at baseline, demonstrated that the PER intervention following CME significantly improved adherence to guidelines compared to CME alone (OR: 1.78, 95% CI: 1.32-2.41). DISCUSSION: The intervention was designed for self-implementation in primary care practices that have their own nursing staff. PER GPs were highly satisfied with the intervention; the majority said that they would implement it in their practice if someone trained their nurse, thus suggesting support for development of a multiprofessional CME program to disseminate this clinical approach to primary care practice groups.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".