NEUROBEHAVIORAL VS FUNCTIONAL MOTOR ASSESSMENT OF THE PRETERM INFANT
Bibliographic record
Abstract
Objectives: Objectives: To compare the Test of Infant Motor Performance (TIMP), a test of functional motor performance with the Einstein Neonatal Neurobehavioral Assessment Scale (ENNAS), an evaluation of newborn neurological integrity. Methods: Design: This was a cross-sectional examination of a prospective cohort at term age. Subjects: Preterm infants born at <32 weeks gestational age and birth weight <1500 grams (n=102) were recruited sequentially from the NICU. Exclusion criteria: Diagnoses of metabolic disorders, cardiac, chromosomal, or congenital abnormalities. Methods: The TIMP and the ENNAS were administered at term on the same day by two experienced occupational therapists using a random order of assignment. The ENNAS emphasizes reflex assessment. The TIMP uses a sequence of selected movements and tasks to represent the functional ecology of the caregiving environment. Results: Results: The ENNAS and the TIMP total scores were strongly correlated (r=-0.64; p<.05), as were the ENNAS total scores and the five TIMP categories of severity: (Spearman r=-0.46). Conclusion: Conclusions: The results suggest that, while the two tests share a similar construct as a measure of neurological integrity, the emphasis of the TIMP on ecological validity has an additional utility for treatment planning. The ENNAS did not offer itself to interpretation for clinical interventions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".