Validity of the Test of Infant Motor Performance for prediction of 6-, 9- and 12-month scores on the Alberta Infant Motor Scale
Bibliographic record
Abstract
The Test of Infant Motor Performance (TIMP) is a test of functional movement in infants from 32 weeks' post-conceptional age to 4 months postterm. The purpose of this study was to assess in 96 infants (44 females, 52 males) with varying risk, the relation between measures on the TIMP at 7, 30, 60, and 90 days after term age and percentile ranks (PR) on the Alberta Infant Motor Scale (AIMS). Correlation between scores on the TIMP and the AIMS was highest for TIMP tests at 90 days and AIMS testing at 6 months (r=0.67, p=0.0001), but all comparisons were statistically significant except those between the TIMP at 7 days and AIMS PR at 9 months. In a multiple regression analysis combining a perinatal risk score and 7-day TIMP measures to predict 12-month AIMS PR, risk, but not TIMP, predicted outcome (21% of variance explained). At older ages TIMP measures made increasing contributions to prediction of 12-month AIMS PR (30% of variance explained by 90-day TIMP). The best TIMP score to maximize specificity and correctly identify 84% of the infants above versus below the 10th PR at 6 months was a cut-off point of 1 SD below the mean. The same cut-off point correctly identified 88% of the infants at 12 months. A cut-off of -0.5 SD, however, maximized sensitivity at 92%. A negative test result, i.e. score above -0.5 SD at 3 months, carried only a 2% probability of a poor 12-month outcome. We conclude that TIMP scores significantly predict AIMS PR 6 to 12 months later, but the TIMP at 3 months of age has the greatest degree of validity for predicting motor performance on the AIMS at 12 months and can be used clinically to identify infants likely to benefit from intervention.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".