Sensitivity and specificity of the Neonatal Visual Assessment to predict motor and cognitive outcomes in infants born very preterm
Bibliographic record
Abstract
BACKGROUND: Very preterm infants are at increased risk of neurodevelopmental impairments. The Neonatal Visual Assessment (NVA) assesses visual function and outcomes and has been used to assess early neurodevelopmental outcomes. This study aimed to compare NVA results of very preterm and term-born infants and to calculate the sensitivity and specificity of the NVA at term equivalent age (TEA) and three months corrected age (CA) to predict motor and cognitive outcomes at 12 months CA in very preterm infants. METHODS: This prospective observational cohort study recruited infants born before 31 weeks gestation and a healthy term-born control group. The NVA was assessed at TEA and three months CA, and neurodevelopmental outcomes (Bayley Scales of Infant and Toddler Development, Third Edition; Neurosensory Motor Developmental Assessment; Alberta Infant Motor Scale) were performed at 12 months CA. The sensitivity and specificity of the NVA to predict outcomes were calculated based on a previously published optimality score. RESULTS: 248 preterm (54 % male) and 46 term-born infants (48 % male) were analysed. The mean NVA scores of preterm and term-born infants were significantly different at TEA (preterm 3.1±2.1; term-born 1.2±1.7, p < 0.001). The NVA had moderate sensitivity (59-78 %) and low specificity (25-27 %) at TEA, and low sensitivity (21-28 %) and high specificity (86-87 %) at three months CA for the prediction of preterm infants' outcomes at 12 months CA. CONCLUSION: The NVA at TEA and three months CA was not a strong predictor of motor and cognitive impairments in this contemporary cohort of very preterm infants.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".