SNAP-II for prediction of mortality and morbidity in extremely preterm infants
Bibliographic record
Abstract
OBJECTIVE: To determine the specific Score of Neonatal Acute Physiology (SNAP-II) cut-off scores associated with outcomes in extremely preterm infants, and to examine its contribution to predictive models that include nonmodifiable birth predictors. STUDY DESIGN: Retrospective observational study of 9240 infants born at 22-28 weeks' gestation and admitted to the Canadian Neonatal Network from 2010 to 2015. Outcomes included early and hospital mortality, composite of mortality/morbidity and individual morbidities. The SNAP-II cut-off to predict each outcome was determined using the Youden index. Additional contributions were evaluated using a base model that adjusted for gestational age, birth weight z-score and sex and by comparing the area under the curve (AUC). RESULTS: The mortality/morbidity rate was 63% (5859/9240). Specific SNAP-II cut-offs ranged from 12 to 20 and were associated with each adverse outcome. Adding SNAP-II cut-offs to predictive models that included birth variables significantly improved (p < .05) the prediction of early mortality (AUC 0.84 versus 0.79), hospital mortality (AUC 0.80 versus 0.78), mortality/morbidity (AUC 0.76 versus 0.75), and severe neurological injury (AUC 0.69 versus 0.66) but had little or no effect on predictive models for retinopathy of prematurity, bronchopulmonary dysplasia, necrotizing enterocolitis, and nosocomial infection. CONCLUSIONS: SNAP-II cut-offs were independently associated with each adverse outcome and using the proposed SNAP-II cut-offs improved the performance of predictive models for certain short-term outcomes.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".