Use of patient-reported outcome measures in physiotherapy clinical trials in six major physiotherapy journals 2 decades apart (2000–2018): a meta-research design
Bibliographic record
Abstract
Aim Using patient-reported outcomes in research has been incentivised to encourage patient-centred care and ensure patient views are considered. We compared the use of patient-reported outcome measures (PROMs) in trials published in physiotherapy journals in 2000 and 2018, and evaluated whether the number of PROMs used differed between musculoskeletal, neurological, and cardiopulmonary subdisciplines.Design Meta-research.Methods Six major physiotherapy journals were searched for trials published in 2000 and 2018. Two independent reviewers extracted data on study characteristics and reporting of PROMs. PROMs were classified according to their outcome domains. Descriptive statistics and inferences were made based on proportions. A 20% difference between 2000 and 2018 was regarded as meaningful.Results A total of 140 trials were included, 39 were published in 2000 and 101 in 2018. Eighty-four percent (n = 118/140) of trials reported ≥1 PROM, while 89% (n = 125/140) included ≥1 non-PROM. We found no meaningful differences on the average use of PROMs in 2000 and 2018: 74% (29/39) of trials in 2000 versus 88% (89/101) in 2018. PROM use in 2000 and 2018 was 88.5% and 84.4% in musculoskeletal physiotherapy, 57.2% and 86.1% in neurological physiotherapy and 0% and 88% in cardiopulmonary physiotherapy. The most used PROM outcome domains were symptoms and symptom burden (75%) and functional status (65%).Conclusion Most trials from the six major physiotherapy journals sampled in 2000 and 2018 used PROMs, with no meaningful differences when comparing years. Fewer publications in 2000 than 2018 may account for the differences seen in neurological and cardiopulmonary physiotherapy.
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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 | 0.116 | 0.287 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.032 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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; 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".