Predicting neurodevelopment in very preterm infants using the Test of Infant Motor Performance
Bibliographic record
Abstract
BACKGROUND: Infants born very preterm (VPT) are at increased risk of neurodevelopmental impairments. The Test of Infant Motor Performance (TIMP) is an assessment used to evaluate an infant's gross motor skills, however, understanding of its predictive accuracy in VPT infants is limited. AIMS: To determine the accuracy of the TIMP assessed at term equivalent age (TEA), and 3 months corrected age (CA), to identify motor or cognitive impairment at 12 months CA in VPT infants. METHOD: This prospective observational cohort study recruited 202 infants born at <31wks gestational age (GA). At TEA and 3 months CA the TIMP was performed. At 12 months CA the following neurodevelopmental assessments were conducted; Alberta Infant Motor Scale (AIMS), Neurological Sensory Motor Development Assessment (NSMDA) and Bayley Scale of Infant and Toddler Development 3rd edition (Bayley III). RESULTS: The TIMP had higher specificity than sensitivity across all four outcome measures. Using a cut off-of ≤ -0.5 at TEA, TIMP z-scores demonstrated low sensitivity and specificity for motor outcomes on the NSMDA (sensitivity 61 %, specificity 50 %), AIMS (sensitivity 59 %, specificity 50 %) and Bayley III (sensitivity 56 %, specificity 51 %). Area under the curve analyses showed that the TIMP assessed at 3 months had greater accuracy than at TEA in identifying neurodevelopmental impairments at 12 months CA. CONCLUSIONS: The TIMP assessed at TEA and 3 months CA correctly identified the majority of VPT infants without motor and cognitive impairments. However, it missed VPT infants who developed adverse neurodevelopmental outcomes by 12 months CA.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".