Physiotherapy interventions for head and trunk control in children with developmental disabilities: A scoping review protocol
Bibliographic record
Abstract
Background: Head and trunk control is prerequisite skill that maximizes engagement and participation in one’s environment by integrating vision, oromotor skill, arm control and respiration. Various physiotherapy and technology-based interventions have been utilized to facilitate head and trunk control in children with developmental disabilities. This scoping review is planned to map and summarize existing studies from the scientific literature on physiotherapy and technology-based interventions for head and trunk control in children with developmental disabilities. Methods: The scoping review will utilize the Joanna Briggs Institute scoping review methodology. The review will cover studies including children and adolescents aged between two years and 17 years 11 months 29 days, with developmental disabilities where in child finds difficulty aligning head and trunk in sitting position, against gravity, for more than a minute. We will include randomized controlled trial (RCT), non-RCT, quasi-experimental trial, and systematic reviews that have employed physiotherapy and technology-based interventions. Database-specific search strategy will be used to search records in Medline (PubMed and Web of Science), Embase, Scopus, CINAHL, PEDro, and Cochrane Library. Additionally, various grey literatures and clinical-trial registries will be searched. Two reviewers, independently, will screen and extract the data. Tables and visual representations will be utilized to present the extracted data. Registration details: The protocol has been registered in Open Science Framework, DOI: 10.17605/OSF.IO/B3RSU (22 nd August 2022)
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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 | 0.071 | 0.057 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.098 | 0.016 |
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".