Physiotherapists Identify Movement Difficulties in Autistic Children Using Subjective and Objective Measures: An Observational Study
Bibliographic record
Abstract
OBJECTIVE: To explore the assessment practices and identification of movement difficulties in autistic children aged 12 months to 6 years by physiotherapists in Australia. METHODS: closed physiotherapy social media (Facebook) pages in Australia from March to June 2022. The survey included 8 items on strategies/approaches to assessment, 47 items on measurement, and 23 items on movement difficulties. RESULTS: 85 physiotherapists completed the survey. Findings indicated that parent reports, observations, and movement analyses were the most commonly used assessment strategies employed 100% of time, followed by musculoskeletal assessments (80%) and standardized assessments (50%). Of standardized assessments used, Alberta Infant Motor Scale (AIMS) was used most, by over 69% of physiotherapists, with multiple versions of five other standardized assessments used by over 52% of physiotherapists, namely Movement Assessment Battery for Children, Neurological Sensory Motor Developmental Assessment, Bruininks-Oseretsky Test of Motor Proficiency, Developmental Coordination Disorder Questionnaire and Test of Gross Motor Development. A range of movement difficulties were frequently identified in developmental delays (86% of time), gross motor (85%), coordination (82%), motor planning (81%), and hypotonia (80%). CONCLUSIONS: Physiotherapists in Australia use a variety of methods to examine movement difficulties in young autistic children, suggesting that this frequently occurs prior to autism diagnosis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".