The Infant Motor Profile: a standardized and qualitative method to assess motor behaviour in infancy
Bibliographic record
Abstract
A reliable and valid instrument to assess neuromotor condition in infancy is a prerequisite for early detection of developmental motor disorders. We developed a video-based assessment of motor behaviour, the Infant Motor Profile (IMP), to evaluate motor abilities, movement variability, ability to select motor strategies, movement symmetry, and fluency. The IMP consists of 80 items and is applicable in children from 3 to 18 months. The present study aimed to test intra- and interobserver reliability and concurrent validity of the IMP with the Alberta Infant Motor Scale (AIMS) and Touwen neurological examination. The study group consisted of 40 low-risk term (median gestational age [GA] 40 wks, range 38-42 wks) and 40 high-risk preterm infants (median GA 29.6 wks, range 26-33 wks) with corrected ages 4 to 18 months (31 females, 49 males). Intra- and interobserver agreement of the IMP were satisfactory (Spearman's rho=0.9). Concurrent validity of IMP and AIMS was good (Spearman's rho=0.8, p<0.005). The IMP was able to differentiate between infants with normal neurological condition, simple minor neurological dysfunction (MND), complex MND, and abnormal neurological condition (p<0.005). This means that the IMP may be a promising tool to evaluate neurological integrity during infancy, a suggestion that needs confirmation by means of assessment of larger groups of infants with heterogeneous neurological conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".