Morbidity and mortality conferences in general surgery: a narrative systematic review
Bibliographic record
Abstract
Background: In medical and surgical departments around the world, morbidity and mortality conferences (MMC) serve dual roles: they are cornerstones of quality-improvement programs and provide timely opportunities for education within the urgent context of clinical care. Despite the widespread adoption of MMCs, adverse events and preventable errors remain high or incompletely characterized, and opportunities to learn from and adjust to these events are frequently lost. This review examines the published literature on strategies to improve surgical MMCs. Methods: We searched OVID Medline, PubMed, Embase and CENTRAL. We defined our combination of search terms using a PICO (population, intervention, comparison, outcome) model, focusing on the use of MMCs in general surgery. Results: The MMC literature focused on 5 themes: educational value, error analysis, case selection and representation, attendance and dissemination. Strategies used to increase educational value included limiting case presentation time to 15-20 minutes, mandatory brief literature reviews, increasing audience interaction, and standardizing presentations using a PowerPoint template or SBAR (situation, background, assessment, recommendation) format. Interventions to improve error analysis included focused discussion on causative factors and taxonomic error analysis. Case selection was improved by using an electronic clinical registry, such as the National Surgery Quality Improvement Program, to better capture incidence of morbidity and mortality. Attendance was improved with teleconferencing. Dissemination strategies included MMC newsletters, incorporating MMCs into plan-do-check-act cycles, and surgeon report cards. Conclusion: Greater standardization of best practices may increase the quality improvement and educational impact of MMCs and provide a baseline to measure the effect of new MMC format innovations on the clinical and educational performance of surgical systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".