Telemonitoring of motor skills using the Alberta Infant Motor Scale for at-risk infants in the first year of life
Bibliographic record
Abstract
INTRODUCTION: Remote assessment creates opportunities for monitoring child development at home. Determining the possible barriers to and facilitators of the quality of telemonitoring motor skills allows for safe and effective practices. We aimed to: (1) determine the quality, barriers and facilitators of Alberta Infant Motor Scale (AIMS) home videos made by mothers; (2) verify interrater reliability; (3) determine the association between contextual factors and the quality of assessments. METHODS: Thirty infants at biological risk aged between three and ten months, of both sexes, and their mothers were included. Assessments were based on asynchronous home videos, where motor skills were evaluated by mothers at home according to AIMS guidelines. The following were analyzed: video quality; stimulus quality; camera position; and physical environment. The video characteristics were analyzed descriptively. The intraclass correlation coefficient was used to calculate interrater reliability and the regression model to determine the influence of contextual factors on the outcome variables. Significance was set at 5%. RESULTS: = 0.980). The contextual factors had no relation with assessment quality. DISCUSSION: Assessments conducted remotely by the mothers showed high video quality and interrater reliability, and represent a promising assessment tool for telemedicine in at-risk infants in the first year of life.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".