Alberta Infant Motor Scale: Cross-cultural analysis of gross motor development in Dutch and Canadian infants and introduction of Dutch norms
Bibliographic record
Abstract
BACKGROUND: The Alberta Infant Motor Scale (AIMS) has been developed in Canada in the 90ies. The AIMS and its Canadian norms are frequently used across the world to monitor infants' gross motor development. Currently, it is disputed whether the Canadian norms are valid for non-Canadian infants. AIMS: To compare scores on the AIMS of Dutch infants with that of the Canadian norms, to compare the sequence of motor milestones in Dutch and Canadian infants, and to establish Dutch AIMS norms. STUDY DESIGN: Cross-sectional study. SUBJECTS: 1697 infants, aged 2-18 months, representative of the Dutch population (gestational age 39.7 weeks (27-42)). OUTCOME MEASURE: AIMS assessments, based on standardized video. Perinatal and social information was obtained by questionnaire and medical records. To create Dutch reference values quantile regression with polynomial splines was used. RESULTS: 1236 Dutch infants (73%) scored below the 50th (P50) percentile of the Canadian norms, 653 (38%) below the P10 and 469 (28%) below the P5. In infants aged 6 to 12 months these values were: 567 infants (81%) < P50, 288 infants (41%) < P10, 201 infants (29%) < P5. The sequence of achievement of motor milestones of Dutch and Canadian infants was similar. Dutch norm-reference values of the AIMS were calculated. CONCLUSIONS AND IMPLICATIONS: Gross motor development of Dutch infants is considerably slower than that of the Canadian AIMS norms sample. To prevent overdiagnosis of developmental delay and overreferral to paediatric physiotherapy Dutch AIMS norms are required. The paper introduces these norms, including percentile ranks.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".