Prediction of Neonatal Outcomes in Extremely Preterm Neonates
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a statistical prediction model spanning the severity range of neonatal outcomes in infants born at ≤ 30 weeks' gestation. METHODS: A national cohort of infants, born at 23 to 30 weeks' gestation and admitted to level III NICUs in Canada in 2010-2011, was identified from the Canadian Neonatal Network database. A multinomial logistic regression model was developed to predict survival without morbidities, mild morbidities, severe morbidities, or mortality, using maternal, obstetric, and infant characteristics available within the first day of NICU admission. Discrimination and calibration were assessed using a concordance C-statistic and the Cg goodness-of-fit test, respectively. Internal validation was performed using a bootstrap approach. RESULTS: Of 6106 eligible infants, 2280 (37%) survived without morbidities, 1964 (32%) and 1251 (21%) survived with mild and severe morbidities, respectively, and 611 (10%) died. Predictors in the model were gestational age, small (<10th percentile) for gestational age, gender, Score for Neonatal Acute Physiology version II >20, outborn status, use of antenatal corticosteroids, and receipt of surfactant and mechanical ventilation on the first day of admission. High model discrimination was confirmed by internal bootstrap validation (bias-corrected C-statistic = 0.899, 95% confidence interval = 0.894-0.903). Predicted probabilities were consistent with the observed outcomes (Cg P value = .96). CONCLUSIONS: Neonatal outcomes ranging from mortality to survival without morbidity in extremely preterm infants can be predicted on their first day in the NICU by using a multinomial model with good discrimination and calibration. The prediction model requires additional external validation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".