Agreement of synchronous remote and in-person application of the Alberta Infant Motor Scale: Cohort study
Bibliographic record
Abstract
IntroductionUsing standardized scales to assess motor development via telemedicine can increase access for low-income populations. Our aim was to verify the agreement and feasibility between remotely and synchronously applying the Alberta Infant Motor Scale (AIMS) and the in-person format.MethodsThis was a concordance study, with 77 typical infants aged 4-18 months (mean = 13 months). The AIMS was applied remote via video calls and face-to-face. We applied a questionnaire to caregivers to verify feasibility.ResultsThere was a high level of agreement between the remote and in-person assessments, with intraclass correlation coefficients above 0.98 and low standard error measure values (<1 item for each posture, <2 items for the total raw score, and =5% for the normative score). The smallest detectable change was between 1.67 and 2.45 for each posture, 3 for the total raw score, and 6% for the normative score. The Bland-Altman analysis showed low bias with the mean difference close to zero (<0.80) and low error with little dispersion of the difference points around the mean. Caregivers' perspectives on the synchronous remote assessment were positive, with good quality, clear information during the assessment, and comfort with the method.DiscussionThe synchronous remote application of the AIMS may be an alternative for families without access to in-person services that assess motor development.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".