Impact of regular mortality and morbidity conferences on preventable death rates.
Bibliographic record
Abstract
Abstract Introduction: The role of mortality and morbidity conferences (M&MC) in surgical departments is to provide education and improve patient care. However, evidence in the literature that M&MCs reduce preventable deaths is sparse. Therefore, this study aimed to assess the impact of routine M&MC on the preventable death rate over four years. Methodology: This study used a quantitative research methodology. In this retrospective audit of the M&MC data, we collected all mortality data from the date the database started, July 2016, to December 2019, for the surgery department. The department adopted and adapted the criteria and definitions of preventability based on WHO guidelines for trauma quality improvement programs. We used the Pearson correlation statistic to evaluate the correlation between the time (years) since the start of routine M&MC and the preventable death rate. We secured ethical approval. Results: There were 4660 registered admissions from July 2016 to December 2019. Of these, 267 deaths were recorded, resulting in a crude mortality rate of 6%. Overall, the department considered 23% (61/267) of the deaths as preventable. A strong linear correlation (R2 = 0.982, p = 0.009) between the preventable death rate and time(years) since the commencement of routine M&MC was found. Trauma was the leading cause of preventable deaths (27.0%, 17/61). Conclusion: Our findings suggest that routine M&MCs have the desired effect of reducing preventable death rates. Further studies are required to investigate this observed effect.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".