Morbidity and mortality audits: "How to"for family practice.
Bibliographic record
Abstract
PROBLEM BEING ADDRESSED: While professions hold their members responsible for self-regulation, many physicians have insufficient information about outcome measures in their practices to judge performance and are inexperienced in performing audits to gather the information they need to judge performance. OBJECTIVE OF PROGRAM: To develop a structure and process to support family doctors with little experience in doing quality improvement studies to conduct morbidity and mortality (M&M) audits. PROGRAM DESCRIPTION: A family medicine teaching group provides members on a rotating basis to an M&M review committee. The committee meets eight times a year and has done four audits, the most comprehensive on the topic of preventable hospital admissions. Both implicit and explicit criteria were incorporated into decision making. Strengths and limitations of the audit process and practice changes that resulted from the audit are discussed. CONCLUSION: Morbidity and mortality audits can vary in rigour. To promote physicians' interest in and commitment to audits, factors considered should reflect the goals, needs, skills, and time available of the physicians involved. Practical learning often results from simple projects.
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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 | 0.063 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.019 | 0.012 |
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".