Bibliographic record
Abstract
Morbidity and mortality conferences (MMCs) have become a vital element of patient care, sitting at the intersection of medical education, quality improvement and risk management. MMCs may have increased in importance as a staple of safety education since the Accreditation Council for Graduate Medical Education has identified that the discussion and analysis of adverse events in a structured fashion promotes the learning of key quality and safety concepts.1–3 Groups across specialties and disciplines have implemented innovative models of MMCs as a vehicle to engage clinicians in discussions to learn from adverse events and to identify opportunities to improve care. In studying these new models, it has become clear that deliberate attention to the structure, processes and content of the conference yields the greatest opportunity for improving the quality of patient care beyond just learning the concepts of quality and safety.4 ,5 We now face the next iteration of the MMC and are tasked with describing the facets that will best allow MMCs to drive learning and improved outcomes. In this issue, Kwok and colleagues highlight the impact of implementing a structured MMC, the Ottawa M&M Model (‘OM3 model’), at their acute care tertiary centre across multiple specialties.6 The model consists of five key elements, including appropriate case selection, structured case analysis, the creation of and dissemination of bottom-line summaries, the development of effective pathways for action items and interprofessional and multidisciplinary participation. The authors conducted a yearlong study of 16 clinical groups implementing the OM3 model. The investigators provided an OM3 toolkit that included relevant educational materials, dedicated coaching to the teams, encouraged the groups to establish a quality committee for subsequent action …
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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 | 0.057 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".