Predicting death and survival without major morbidity for extremely preterm infants using information on hospital admission: a multicenter cohort study
Bibliographic record
Abstract
Background: Accurate prediction of outcomes for extremely preterm infants (EPIs) during the early stage is important to assist clinicians and parents in making decisions. This study aimed to develop and validate models for predicting mortality and survival without major morbidity for EPIs using information available on neonatal intensive care units (NICUs) admission. Methods: weeks' gestation were included in China. Two predictive models were generated separately to predict mortality and survival without major morbidity at discharge. Potential predictors were identified if they had a well-established association with neonatal outcomes in literatures and could be easily obtained on NICU admission, including gestational age, birth weight, sex, inborn, antenatal steroids, 5-min Apgar score, and invasive ventilation on admission. Logistic regression was employed to develop the models. Model performance was assessed via area under the curve (AUC). Results: Among 2,438 EPIs in the development cohort, the mortality rate was 17.7% (431/2,438) and the rate of survival without major morbidity was 52.5% (1,281/2,438). Among the 5,045 infants in the validation cohort, 9.2% (463/5,045) died, and 59.1% (2,981/5,045) survived without major morbidity. Gestational age, birth weight, invasive ventilation on NICU admission, antenatal steroids use, and 5-min Apgar score were selected as predictors in the mortality model, yielding the AUC of 0.77 [95% confidence interval (CI): 0.75-0.79]. For the survival without major morbidity model, predictors were gestational age, birth weight, invasive ventilation on NICU admission, sex, and 5-min Apgar score, and the AUC was 0.72 (95% CI: 0.70-0.74). The validation cohort resulted in AUCs of 0.76 (95% CI: 0.73-0.78) and 0.70 (95% CI: 0.68-0.71) for the mortality and survival without major morbidity models, respectively. Conclusions: Using commonly available predictors on NICU admission including gestational age, birth weight, invasive ventilation on NICU admission, antenatal steroids use, sex, and 5-min Apgar score, we successfully developed and validated two distinct models with acceptable performance, predicting mortality and survival without major morbidity for EPIs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".