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 …
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 | 0.004 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".