MÉTODOS DE AVALIAÇÃO FISIOTERAPÊUTICA PARA CRIANÇAS COM TRANSTORNO DO ESPECTRO AUTISTA
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in communication, social interaction, and behavior, which may also affect motor development and sensory integration. This study aimed to identify and analyze the main physiotherapeutic assessment tools applicable to children with ASD, emphasizing their relevance for therapeutic planning. A systematic review was conducted in the LILACS, SciELO, PubMed, PEDro, MedLine, Cochrane, and BIREME databases, using the descriptors “autism,” “children,” “physiotherapy,” and “assessment,” combined with the Boolean operator AND. A total of 696 articles were identified, and after applying eligibility criteria, 26 studies published between 2015 and 2025 were included. The results revealed a predominance of systematic reviews and observational studies, highlighting validated instruments such as the GMA-AUT checklist, the Alberta Infant Motor Scale (AIMS), and the Quantitative Sensory Testing (QST), as well as general movements (GM) assessment for early detection. Evidence indicates that standardized and early evaluations allow for more effective interventions, promoting social, motor, and adaptive development. The studies also emphasize the need for evaluator training and for expanding clinical research with stronger methodological rigor. It is concluded that physiotherapy plays an essential role in the assessment and intervention of children with ASD, and should integrate motor and sensory approaches from early childhood to enhance autonomy, functionality, and overall quality 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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".