Morbidity and Mortality Conference: Its Purpose Reclaimed and Grounded in Theory
Bibliographic record
Abstract
ISSUE: The morbidity and mortality conference (MMC) remains a central activity within the departments of our academic healthcare institutions. It is deeply rooted in the premise that we can learn from our mistakes, thereby improving the care we provide. Recent advances in our understanding of medical error and quality improvement have challenged the value of traditional models of MMC. As a result the purpose of MMC has become clouded and ill-defined: Is it an educational conference that promotes mastery of clinical acumen, or is it a venue to drive quality improvement by addressing systems-based issues in delivering care? Or can it serve both purposes? EVIDENCE: Review of the history of MMC, the literature, and critical application of education theory demonstrates the source of the confusion and the challenges in viewing it through the exclusive lens of either education or quality improvement. Application of experiential learning theory helps resolve this discord showing how the conference facilitates the development of clinical mastery while informing quality improvement programs about important and relevant systems-based issues. IMPLICATION: Building on this, we present a model for MMC involving five essential elements: case-based involving an adverse patient event, anonymity for participants, expert guided critical analysis, reframing understanding of the case presentation and related systems-based factors, and projection to practice change. This model builds on previously described models, is grounded in the literature, and helps clarify its role from both the educational and the quality improvement perspectives.
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 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 | 0.076 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.047 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.016 |
| 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".