Use of the Alberta Infant Motor Scale to Characterize the Motor Development of Infants Born Preterm at Eight Months Corrected Age
Bibliographic record
Abstract
The Alberta Infant Motor Scale (AIMS) was used to examine variations in motor development of infants born preterm. Sixty infants attending a Developmental Follow-up Clinic participated. Infants were assessed by physical therapists using the AIMS and independently judged by physicians to be neurodevelopmentally and neurologically “normal,” “suspect,” or “abnormal.” The AIMS clearly differentiated infants in these three categories. Compared to the normative sample, infants judged to be “normal” demonstrated similar motor behaviors, infants judged to be “abnormal” were significantly different across a wide range of items, and infants judged to be “suspect” were significantly different on items requiring antigravity postural control, lower extremity dissociation, and trunk rotation. The AIMS can be used to identify infants developing abnormally, to affirm normalcy in infants developing typically, and to identify motor differences in infants who are neurologically “suspect.” In the latter group of infants, the AIMS can be used to provide anticipatory guidance to parents regarding the components of movement they might expect their infants to be developing next.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 |
| 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".