Predictive Validity of the Premie-Neuro at 3 Months' Adjusted Age and 2 Years' Chronological Age
Bibliographic record
Abstract
BACKGROUND: It is difficult to predict which preterm babies are most at risk for poor neurodevelopmental outcomes. A quick, highly predictive assessment tool would aid neonatal clinicians in making decisions about follow-up care. PURPOSE: The purpose of this study was to determine whether performance on the Premie-Neuro in the neonatal intensive care unit predicted neurodevelopmental outcomes at 3 months' adjusted age and 24 months' chronological age. METHODS: Thirty-four preterm infants were administered the Premie-Neuro in the neonatal intensive care unit. Infants were assessed using the Infanib and Alberta Infant Motor Scales at 3 months' adjusted age, and using the Bayley Scales of Infant and Toddler Development, Third edition (Bayley-III) at 24 months' chronological age. Scores were analyzed to determine whether Premie-Neuro performance at less than 37 weeks postmenstrual age was predictive of neurodevelopmental outcomes at 3 months' adjusted age and 24 months' chronological age. RESULTS: Premie-Neuro raw scores were predictive of outcomes at 3 months' adjusted age and 24 months' chronological age. Premie-Neuro classifications were not predictive of Infanib and Alberta Infant Motor Scale classifications at 3 months' adjusted age but were predictive of Bayley-III classification at 24 months' chronological age. IMPLICATIONS FOR PRACTICE: Premie-Neuro raw scores may be used by the clinician to identify infants at risk for neurodevelopmental delays. Premie-Neuro classifications should be interpreted cautiously. IMPLICATIONS FOR RESEARCH: More research is needed to determine whether the Premie-Neuro may be used as an adjunct to clinical assessment to identify infants who are most at risk for developmental delay.
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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 | 0.000 |
| 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".