Reliability of the Alberta Infant Motor Scale (AIMS) When Used via Telehealth for Neurodevelopmentally High-Risk Infants
Bibliographic record
Abstract
AIMS: recorded telehealth sessions by novice and expert raters. METHODS: Ten assessors (six novice, four expert) independently rated recorded telehealth assessments of 23 neurodevelopmentally high-risk infants twice. Inter- and intra-rater reliability of subscale scores, total score and percentile rankings were determined. RESULTS: AIMS total score inter-rater reliability was excellent across all raters (ICC = 0.92-0.96). Inter-rater-reliability across prone, supine and sitting subscale scores was excellent (ICC = 0.90-0.96) but variable for standing subscale (ICC = 0.06-0.65). Novice total score intra-rater reliability was variable (ICC = 0.45-0.94); expert reliability was excellent (ICC = 0.93-1.00). Recording to real-time telehealth assessment had excellent intra-rater reliability (ICC = 0.96). Time taken to complete the assessment was comparable to a face-to-face assessment (mean: 14.9 min). Novices paused/replayed each video more than experts (2.2 compared to 1.0 in Time 1; and 1.0 compared to 0.5 in Time 2). CONCLUSIONS: telehealth consultation. Time taken to complete the assessment is comparable to a face-to-face assessment. Novice inter-rater reliability was similar to experts. Training and the ability to pause/review infant motor performance may explain the accuracy achieved.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".