Implementation of the Ottawa morbidity and mortality model improves the quality of morbidity and mortality rounds
Bibliographic record
Abstract
Objective: To determine the effect of implementing the Ottawa morbidity and mortality (M&M) model (OM3) on the perceived quality of M&M rounds. Methods: This was an observational study that included faculty, house officers, and staff, performed from September 2021 through June 2023, in conjunction with implementation of the OM3. A survey was emailed to participants at the beginning of the study period (PRE), at the end of the first academic year (POST-Y1), and at the end of the second academic year (POST-Y2). During the first year (September 2021 through May 2022), a separate survey to evaluate each individual session of rounds was emailed to participants. Categorical data are described using counts and percentages. Results: There were 67 participants in the PRE survey, 50 in the POST-Y1 survey, and 70 in the POST-Y2 survey. From PRE to POST-Y1, findings included (1) a 19% (22% PRE to POST-Y2) increase in "agree" responses when asked if M&M rounds had a significant impact on the quality of care they provide on a section/service level, (2) a 25% (13% PRE to POST-Y2) increase in the average percentage of M&M rounds thought to effectively address cognitive issues, and (3) a 25% (20% PRE to POST-Y2) increase in the average percentage of M&M rounds thought to effectively address systemic issues. Conclusions: The implementation of the OM3 was perceived by respondents to improve patient care at the individual and service levels. Additionally, it more effectively addressed cognitive and systemic issues. Clinical Relevance: The OM3 is a straightforward and impactful M&M model to institute, and its implementation should be considered in other veterinary practices.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".