Identifying Systems Developed for Classifying Physiotherapy Interventions in Neurological Rehabilitation: A Scoping Review
Bibliographic record
Abstract
Purpose: The purpose of the study was to conduct a scoping review of classification systems developed for physiotherapy interventions of persons with neurological conditions, describing the information captured, organizational structure, and methods used in development. Method: Five electronic databases and grey literature were searched, three journals were hand searched, and all articles identified in electronic databases were forward searched. All article types except conference proceedings were considered. Articles were included if they were in English and described: a classification system developed to capture physiotherapy interventions, the contents of the classification system, and its use with neurological patient populations. Results: Twenty unique classification systems were identified that differed greatly in the amount of intervention detail described and in how they were developed and structured. Conclusions: There is significant heterogeneity in the amount of detail and structure between the classification systems. There is a need for continued work to develop a system or refine an existing system. A system should describe therapy activities in sufficient detail for communication and evaluative purposes, while considering the feasibility and acceptability across various contexts to ensure successful implementation.
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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 | 0.112 | 0.272 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.065 | 0.057 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".