Using internal and external reviewers can help to optimise neonatal mortality and morbidity conferences
Bibliographic record
Abstract
AIM: This study determined whether there was a difference in the conclusions reached by neonatologists in morbidity and mortality conferences based on their level of involvement in a case. METHODS: All neonatal deaths occurring between August 2014 and September 2015 at the neonatal intensive care unit of Sainte-Justine Hospital, Montreal, Quebec, Canada, were reviewed by internal physicians involved in the case and external physicians who were not. The reviewers were asked to identify positive and negative clinical practice items and provide written recommendations. These were classified into eight categories and compared for each case. RESULTS: During the study, 55 patients died leading to 110 reviews and a total of 590 positive and negative items. Most items were in the communication (25.2%), ethical decision-making (16.7%) and clinical management (14.8%) categories. Both the internal and external reviewers were in agreement 48.5% of the time for positive items and 44.8% for negative items. There were 242 written recommendations, which differed significantly among the internal and external reviewers. CONCLUSION: Reviews of neonatal deaths by two independent reviewers, internal physicians and external physicians, led to different positive and negative practice items and recommendations. This could allow for a richer discussion and improve recommendations for patient care.
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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 | 0.382 | 0.703 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".