Assessment of the Reliability and Validity of the Alberta Infant Motor Scale and Peabody Development Motor Scale in High-risk Infants
Bibliographic record
Abstract
Introduction: The aim of this study was to investigate the correlation between Alberta Infant Motor Scale (AIMS) scores at 8 months and Peabody Developmental Motor Scale-2 (PDMS-2) and Peabody Developmental Gross Motor Scale-2 (PDMS-GM-2) scores at 18 months and 3 years in high-risk infants. Methods: This retrospective study included 105 high-risk infants at a Saudi Arabian tertiary care facility. Pearson correlation analysis was used for comparing scores. Additional subgroup analyses were performed for participants diagnosed with cerebral palsy at 18 months and for those who received physiotherapy. Result: AIMS scores at 18 months showed stronger correlations with PDMS-GM-2 scores than with PDMS-2 total scores, while these correlations decreased at 3 years. For the cerebral palsy subgroup, correlation with PDMS-2 scores at 18 months was relatively stronger than at 3 years. For the physiotherapy intervention subgroup, correlations with PDMS-GM-2 scores PDMS-2 total scores were similar at 18 months and 3 years. Conclusion: The AIMS predictive validity was lowest at 3 years in high-risk infants. A correlation was higher in participants with physiotherapy intervention and highest in participants with cerebral palsy. Outcome measures and treatment results should be cautiously reported during the first 3 years to prevent over-treating high-risk infants and decrease rehabilitation costs.
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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 | 0.004 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".