Reliability and concurrent validity of the <scp>I</scp>nfant <scp>M</scp>otor <scp>P</scp>rofile
Bibliographic record
Abstract
AIM: The Infant Motor Profile (IMP) is a qualitative assessment of motor behaviour in infancy. It consists of five domains: movement variation, variability, fluency, symmetry, and performance. The aim of this study was to assess interobserver reliability and concurrent validity of the IMP with the Alberta Infant Motor Scale (AIMS) and an age-specific neurological examination. METHOD: Fifty-nine preterm infants (25 females, 34 males; median gestational age 29.7wks, median birthweight 1285g) and 146 term infants (74 females, 72 males; median gestational age 40.1wks, birthweight 3500g) were included. Assessments were performed at corrected ages of 4, 6, 10, 12, and 18 months and consisted of the IMP, AIMS, and an age-specific neurological examination. Interobserver reliability was investigated on a sample of 25 video recordings. Non-parametric statistics were used to analyse the data. RESULTS: Interobserver reliability was high (intraclass correlation coefficient 0.95). At all ages, AIMS scores correlated weakly to fairly with total IMP scores (Spearman's ρ 0.36-0.55), but moderately to strongly with scores on the performance domain of the IMP (Spearman's ρ 0.47-0.84). A clear relation was found between total IMP score and outcome of the neurological examination (Kruskal-Wallis p<0.001 at all ages). INTERPRETATION: Interobserver reliability of the IMP is good. Concurrent validity with the AIMS is best for the IMP performance domain. Concurrent validity with age-specific neurological examination is very good.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".