Measuring What Matters at Morbidity and Mortality Conferences: A Scoping Review of Effectiveness Measures
Bibliographic record
Abstract
OBJECTIVE: Efforts to study morbidity and mortality conferences (M&MC) are hampered by the lack of rigorous instruments to assess the effectiveness of the conferences for the purpose of quality improvement and medical education. This might limit further advancement of the practice. The aim of this scoping review was to determine commonly used effectiveness measures of M&MC in the literature. METHOD: A scoping review was performed of quantitative, qualitative, and mixed methods studies of M&MC, using databases from PubMed, Emcare, Embase, Web of Science, and the Cochrane library. Studies were included if an outcome was described after a general evaluation or an intervention to M&MC. Study quality was assessed with the Quality Assessment Tool for Studies with Diverse Designs. RESULTS: A total of 43 articles were included in the review. The majority used a quantitative (n = 23) or mixed (n = 17) design, with surveys as the most frequent method used for data collection (n = 29). The overall Quality Assessment Tool for Studies with Diverse Designs scores were modest (64%). Outcome measures used to evaluate the effectiveness of M&MC were clustered in the following categories: "participant experiences," "characteristics of the meeting," "medical knowledge," "actions for improvement," and "clinical outcomes." CONCLUSIONS: This review found a wide variety of effectiveness measures for M&MC. Rather than using isolated measures, approaches that combine multiple effectiveness measures could offer a more comprehensive assessment of M&MC. Although there was a preference for quantitative metrics, this fails to seize the opportunity of qualitative methods to yield insights into sociological purposes of M&MC, such as building professional identities and safety culture.
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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 | 0.133 | 0.342 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.033 | 0.024 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".