Continued improvement in morbidity reduction in extremely premature infants
Bibliographic record
Abstract
OBJECTIVE: Provide a progress report updating our long-term quality improvement collaboration focused on major morbidity reduction in extremely premature infants 23-27 weeks. METHODS: 10 Vermont Oxford Network (VON) neonatal intensive care units (NICUs) (the POD) sustained a structured alliance: (A) face-to-face meetings, site visits and teleconferences, (B) transparent process and outcomes sharing, (C) utilisation of evidence-based potentially better practice toolkits, (D) family integration and (E) benchmarking via a composite mortality-morbidity score (Benefit Metric). Morbidity-specific toolkits were employed variably by each NICU according to local priorities. The eight major VON morbidities and the risk-adjusted Benefit Metric were compared in two epochs 2010-2013 versus 2014-2018. RESULTS: 5888 infants, mean (SD) gestational age 25.8 (1.4) weeks, were tracked. The POD Benefit Metric significantly improved (p=0.03) and remained superior to the aggregate VON both epochs (p<0.001). Four POD morbidities significantly improved through 2018 - chronic lung disease (48%-40%), discharge weight <10th percentile (32%-22%), any late infection (19%-17%) and periventricular leukomalacia (4%-2%). In epoch 2, 34% of survivors had none of the eight major morbidities, while 36% had just one. Mortality did not change. CONCLUSIONS: Inter-NICU collaboration, process and outcomes sharing and potentially better practice toolkits sustain improvement in 23-27 week morbidity rates, notably chronic lung disease, extrauterine growth restriction and the lowest zero-or-one major morbidity rate reported by a quality improvement collaboration. Unrevealed biological and cultural variables affect morbidity rates, countless remain unmeasured, thus duplication to other quality improvement groups is challenging. Understanding intensive care as innumerable interactions and constant flux that defy convenient linear constructs is fundamental.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".