Comparing Two Motor Assessment Tools to Evaluate Neurobehavioral Intervention Effects in Infants With Very Low Birth Weight at 1 Year
Bibliographic record
Abstract
BACKGROUND: Infants with very low birth weight (VLBW) are at increased risk for motor deficits, which may be reduced by early intervention programs. For detection of motor deficits and to monitor intervention, different assessment tools are available. It is important to choose tools that are sensitive to evaluate the efficacy of intervention on motor outcome. OBJECTIVE: The purpose of this study was to compare the Alberta Infant Motor Scale (AIMS) and the Psychomotor Developmental Index (PDI) of the Bayley Scales of Infant Development-Dutch Second Edition (BSID-II-NL) in their ability to evaluate effects of an early intervention, provided by pediatric physical therapists, on motor development in infants with VLBW at 12 months corrected age (CA). DESIGN: This was a secondary study in which data collected from a randomized controlled trial (RCT) were used. METHODS: At 12 months CA, 116 of 176 infants with VLBW participating in an RCT on the effect of the Infant Behavioral Assessment and Intervention Program were assessed with both the AIMS and the PDI. Intervention effects on the AIMS and PDI were compared. RESULTS: Corrected for baseline differences, significant intervention effects were found for AIMS and PDI scores. The highest effect size was for the AIMS subscale sit. A significant reduction of abnormal motor development in the intervention group was found only with the AIMS. LIMITATIONS: No Dutch norms are available for the AIMS. CONCLUSIONS: The responsiveness of the AIMS to detect intervention effects was better than that of the PDI. Therefore, caution is recommended in monitoring infants with VLBW only with the PDI, and the use of both the AIMS and the Bayley Scales of Infant Development is advised when evaluating intervention effects on motor development at 12 months CA.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".