A systematic review of the clinimetric properties of neuromotor assessments for preterm infants during the first year of life
Bibliographic record
Abstract
This systematic review evaluates assessments used to discriminate, predict, or evaluate the motor development of preterm infants during the first year of life. Eighteen assessments were identified; nine met the inclusion criteria. The Alberta Infant Motor Scale (AIMS), Bayley Scale of Infant and Toddler Development -- Version III, Peabody Developmental Motor Scales -- Version 2, Test of Infant Motor Performance (TIMP), and Toddler and Infant Motor Examination have good discriminative validity when examined in large populations. The AIMS, Prechtl's Assessment of General Movements (GMs), Neuro Sensory Motor Development Assessment (NSMDA), and TIMP were designed for preterm infants and are able to detect more subtle changes in movement quality. The best predictive assessment tools are age dependent: GMs, the Movement Assessment of Infants, and TIMP are strongest in early infancy (age 4 mo or less) and the AIMS and NSMDA are better at older ages (8-12 mo). The TIMP is the only tool that has demonstrated a difference between groups in response to intervention in two randomized controlled trials. The AIMS, TIMP, and GMs demonstrated the highest levels of overall reliability (interrater and intrarater intraclass correlation coefficient or kappa>0.85). Selection of motor assessment tools during the first year of life for infants born preterm will depend on the intended purpose of their use for discrimination, prediction, and/or evaluation.
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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 | 0.011 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".