Pediatric Assessments for Preschool Children in Digital Physical Therapy Practice: Results From a Scoping Review
Bibliographic record
Abstract
PURPOSE: To examine and map the extent and scope of pediatric physical therapy assessments previously used in the digital context. METHODS: A 6-step evidence-based scoping methodological framework was used. Articles containing assessments conducted by a physical therapist using technology to assess a child aged 0 to 5 years were included and synthesized using descriptive statistics and thematic analysis. RESULTS: Eighteen studies identifying 25 assessments were eligible. Asynchronous observational developmental instruments administered in the child's natural environment to those at risk or presenting with neurodevelopmental conditions were the most common. There is a need for detailed procedures and training for caregivers and clinicians. CONCLUSION: Limited research exists on the use of pediatric physical therapy assessments for young children with musculoskeletal and cardiorespiratory conditions in a digital context. The development of new instruments or modifications of existing ones should be considered and be accompanied by detailed administration protocols and user guides.
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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 | 0.016 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".