Implementation of a Structured Morbidity and Mortality Rounds on an Internal Medicine Clinical Teaching Unit
Bibliographic record
Abstract
Morbidity and mortality rounds (MMRs) are widely used in a variety of medical settings; however, their implementation and quality are highly variable. In this quality improvement project, we implemented the Ottawa Morbidity and Mortality Model (OM3) at a quaternary teaching hospital inpatient internal medicine clinical teaching unit (CTU). We assessed adherence to the model using a locally developed scale and surveyed participants regarding acceptability of the change. We used several measures to improve adherence, including regular involvement of allied health professionals, screening of cases for appropriateness, providing a template to the residents who prepare cases for presentation, and limiting the number of presentations at each session. Adherence to OM3 improved over time and was consistently high by the end of our data-collecting period. The intervention was also widely accepted by participants, and rounds were found to be valuable to participants. Implementing a validated, structured format, such as OM3, can improve the quality of MMRs while being accepted by participants in an internal medicine teaching hospital.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".