Relationship between early infant motor repertoire and neurodevelopment on the hammersmith infant neurological examination in a developmentally vulnerable First Nations cohort
Bibliographic record
Abstract
AIM: To implement a culturally-adapted screening program aimed to determine the ability of infant motor repertoire to predict early neurodevelopment on the Hammersmith Infant Neurological Examination (HINE) and improve Australian First Nations families' engagement with neonatal screening. METHODS: weeks (V1; <14 weeks) and 14-18 weeks (V2; ≥14 weeks) corrected age (CA). At 4-9 months CA neurodevelopment was assessed on the HINE and classified according to age-specific cut-off and optimality scores as; developmentally 'on track' or high chance of either adverse neurodevelopmental outcome (ad-NDO) or cerebral palsy (CP). RESULTS: Families were highly engaged, 139/148 (94 %) eligible infants completing MOS-R, 136/150 (91 %), HINE and 123 (83 %) both. Lower MOS-R at V2 was associated with reduced HINE scores (β = 1.73, 95 % confidence interval [CI] = 1.03-2.42) and high chance of CP (OR = 2.63, 95%CI = 1.21-5.69) or ad-NDO (OR = 1.38, 95%CI = 1.10-1.74). The MOS-R sub-category 'observed movement patterns' best predicted HINE, infants who score '4' had mean HINE 19.4 points higher than score '1' (95%CI = 12.0-26.9). Receiver-operator curve analyses determined a MOS-R cut-off of <23 was best for identifying mild to severely reduced HINE scores, with diagnostic accuracy 0.69 (sensitivity 0.86, 95%CI 0.76-0.94 and specificity 0.40, 95 % CI 0.25-0.57). A trajectory of improvement on MOS-R (≥2 point increase in MOS-R from 1st to 2nd video) significantly increased odds of scoring optimally on HINE (OR = 5.91, 95%CI 1.16-29.89) and may be a key biomarker of 'on track' development. INTERPRETATION: Implementation of a culturally-adapted program using evidence-based assessments demonstrates high retention. Infant motor repertoire is associated with HINE scores and the early neurodevelopmental status of developmentally vulnerable First Nations 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".