Assessment of motor development using the Alberta Infant Motor Scale in full-term infants
Bibliographic record
Abstract
The Alberta Infant Motor Scale (AIMS) is a well-known, norm-referenced scale that evaluates the gross motor development of children from birth to 18 months. The aim of the study was to compare the Canadian norms with the AIMS scores of a Turkish sample of infants, and to investigate whether the current reference values of the AIMS are representative for Turkish full-term infants. The study was conducted with 411 Turkish infants of both sexes (195 girls and 216 boys), born with gestational age 38 weeks and older, weighing ≥2500 g at birth. Motor performance of all the cases at different ages were assessed with the AIMS which was used by a physiotherapist. The mean AIMS scores of Turkish infants were compared with the norm values of the original AIMS established in a Canadian sample of infants. The results showed no statistically significant differences between the AIMS scores of Turkish and Canadian infants during the first 18 months of life except at 0- < 1 and 2- < 3 months of age. The AIMS scores were significantly lower in Turkish infants than in Canadian infants at 0- < 1 (p=0.025) and 2- < 3 (p=0.042) months of age. In conclusion, the AIMS can be used in Turkish children to assess gross motor development, especially after 4 months of age. However, this paper was presented as a preliminary study to compare AIMS results between Turkish and Canadian infants, and further studies are needed to realize the Turkish validation of AIMS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".