Mortality and Morbidity rounds in neonatology: Providers’ experiences and perspectives
Bibliographic record
Abstract
AIM: To describe how Canadian level III neonatal intensive care units (NICU) organise mortality and morbidity rounds (M&MR) and explore clinicians' perspectives. METHODS: This questionnaire study, including open-ended questions, examined the following domains: (1) M&MR format, (2) ethical issues and (3) limitations and perceived effectiveness. RESULTS: Sixteen out of twenty (80%) level III NICUs participated. All deaths and 64% of morbidities were discussed. M&MR occurred monthly (69%) with 3-5 patients discussed hourly (63%) and usually (75%) physician led. Wide variations of practice between centres existed for practical issues, such as administrative support and attendance. 44% of centres allowed nurses to participate. Goals reported by participants were also heterogeneous: reducing medical error (56%), educational (50%), improving communication (44%) and peer review (23%). Practical barriers were time (75%) and lack of resources/structure (25%). Four main themes were as follows: the role of M&MR, the ongoing blame culture, communication issues and the distinction between mortality and morbidity. CONCLUSION: Goals and format of M&MR vary widely. M&MR remains physician-centric, where the blame culture still endures. Neonatal M&MR models should be adapted to the modern NICU to ensure the M&MR stays relevant. It could also benefit from lessons learned in quality improvement.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".