Concurrent validity of the Neurobehavioural Assessment for Pre-term Infants (NAPI) at term age
Bibliographic record
Abstract
BACKGROUND: Accurate measurement of neonatal neurological integrity is critical for early identification of pre-term and full-term infants at-risk for developmental disability. The Neurobehavioural Assessment for Pre-term Infants (NAPI) was developed to measure the progression of neurobehavioural development in pre-term infants born between 32 weeks post-conceptional age (PCA) and term. This instrument has many unique advantages; however, criterion validity is unknown and results are subsequently difficult to interpret. OBJECTIVES: This study examined the concurrent validity of the NAPI against a criterion instrument, the Einstein Neonatal Neurobehavioural Assessment Scale (ENNAS), which measures similar constructs and has demonstrated excellent reliability and validity. METHODS: A sample of 41 pre-term and full-term infants (40 +/- 2 weeks) was assessed with the NAPI and ENNAS on the same day. RESULTS: The findings demonstrated that correlations between similar NAPI clusters and ENNAS clusters ranged from 0.35-0.65 and correlations between many similar individual NAPI and ENNAS items ranged from 0.40-0.60. Two NAPI clusters also discriminated between normal, abnormal and suspect performance on the ENNAS. CONCLUSION: The NAPI has many unique advantages as a tool. It examines neonates serially, has established weekly normative data and requires minimal infant handling. This study provides new validation of the NAPI instrument.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".