The Quality of Reports of Randomized Controlled Trials Varies between Subdisciplines of Physiotherapy
Bibliographic record
Abstract
PURPOSE: The quality of reports of randomized trials of physiotherapy interventions varies by year of publication, language of publication and whether the intervention being assessed is a type of electrotherapy. Whether it also varies by subdiscipline of physiotherapy has not yet been systematically investigated. The purpose of this study was to determine whether the quality of trial reports varies according to the subdiscipline of physiotherapy being evaluated. METHODS: Reports of physiotherapy trials were identified using the Physiotherapy Evidence Database (PEDro). Quality of the trial report was evaluated using the PEDro scale (total PEDro score and 11 individual PEDro scale items). Multiple linear and logistic regressions were used to predict the quality of trial reports, with subdisciplines, time since publication, language of publication, and evaluation of electrotherapy as independent variables in the model. RESULTS: Total PEDro scores are higher when trial reports are more recent; are published in English; investigate electrotherapy; and are in the subdisciplines of musculoskeletal, neurology, cardiopulmonary, gerontology, continence and women's health, orthopaedics, or paediatrics. Trials in the subdisciplines of ergonomics and occupational health, oncology, and sports are associated with lower total PEDro scores. The musculoskeletal subdiscipline had a positive association with six of the PEDro scale items, more than any other subdiscipline. CONCLUSIONS: There is scope to improve the quality of the conduct and reporting of randomized trials across all the physiotherapy subdisciplines. This study provides specific information about how each physiotherapy subdiscipline can improve trial quality.
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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 | 0.711 | 0.929 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".