Infusing Evidence-Based Practice Into Interdisciplinary Perinatal Morbidity and Mortality Conferences
Bibliographic record
Abstract
Incorporating evidence-based practice into the hospital setting has been a challenge but is needed to deliver quality healthcare. Interdisciplinary morbidity and mortality conferences are used to discuss perinatal and neonatal care issues with high-risk and low-frequency cases, such as fetal demise, maternal death, or identified areas for improvement. By involving an interdisciplinary team to review the patient's case, a more holistic perspective of the patient's care will be achieved. The purpose of this article is to demonstrate how nurses can be an essential part of the interdisciplinary morbidity and mortality conferences and how to infuse evidence-based practice into the conference. A perinatal morbidity and mortality conference will be described to illustrate how one maternal-neonatal department brought medicine and nursing together to review 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 | 0.411 | 0.611 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.007 | 0.041 |
| Research integrity | 0.017 | 0.035 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".