Prediction of Outcome at School Age in Neonatal Intensive Care Unit Graduates Using Neonatal Neurologic Tools
Bibliographic record
Abstract
Prediction of outcome for neonatal intensive care unit graduates is clinically useful to counsel families effectively and target those who may benefit from early interventions. Evoked potentials have proven prognostic value of neurologic outcomes in early childhood; however, their long-term predictive validity remains to be determined. The objective of this prospective study was to determine the long-term predictive value of three neonatal neurologic assessments: brainstem auditory evoked potentials, somatosensory evoked potentials, and the Einstein Neonatal Neurobehavioral Assessment Scale. Seventy-eight high-risk newborns and 28 healthy controls were recruited and were assessed in the newborn period using these tests. At 8 to 9 years of age, 42 subjects and 13 controls were re-evaluated for developmental progress using a range of psychologic, sensorimotor, and neurologic measures. Findings indicated that the somatosensory evoked potential was most accurate at predicting outcome at school age, with high specificity (83-100%) across all domains tested and good sensitivity (80-100%) for intellectual performance and sensorimotor abilities. The brainstem auditory evoked potential was limited by false-negatives, whereas the neonatal neurobehavioral assessment yielded many false-positives. This study provides new evidence that associations between neonatal somatosensory evoked potentials and developmental sequelae continue to be significant at school age.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".