Assessing the quality of research outputs in physiotherapy
Bibliographic record
Abstract
Assessing the quality of research output is challenging. Systematic reviews and meta-analyses of randomized trials have a pivotal role in informing clinical practice and policy decisions, and there is a broad agreement that the method quality of primary research should be carefully assessed (Albanese et al., 2020). One of the instruments to do that is the Physiotherapy Evidence Database (PEDro) scale, specifically designed to assess the quality of methods used in clinical trials in the field of physiotherapy (Elkins et al., 2013). Its reliability in terms of ICC values ranges from 0.55 (95% confidence interval CI: 0.47–0.65) for the original scale, to 0.82 (95% CI: 0.70–0.89) for the Portuguese version (Shiwa et al., 2011). The PEDro scale consists of 11 items encompassing the domains of external validity (item 1: Eligibility criteria and source), internal validity (items 2 to 9: Random allocation; Concealed allocation; Baseline comparability; Blinding of participants; Blinding of therapists; Blinding of assessors; Adequate follow-up (>85%); and Intention-to-treat analysis), and statistical reporting (items 10 and 11: Betweengroup statistical comparisons and Reporting of point measures and measures of variability) (Cashina, McAuleya, 2020). Each item is scored as either present (1) or absent (0), leading to a maximum score of 10 (Paci, Bianchini and Baccini, 2022). A trial is considered of moderate to high quality if it scores at least 6 of 10 (Paci, Bianchini and Baccini, 2022). The purpose of the PEDro score is to help researchers identify trials that have good internal validity (items 2–9) and that report enough data to make their results interpretable (items 10 and 11) (Moseleya et al., 2020). Interpreting these items correctly is critical to high-quality, evidence-based health practice. Unlike PEDro, however, the Cochrane Collaboration distinguishes between the methodological quality of a study and the risk of bias: a study of high quality can still be at high risk of bias (Higgins et al., 2011). The Cochrane risk of bias (RoB) tool focuses on the internal validity of trials and assesses six domains of bias: selection bias, performance bias, detection bias, attrition bias, reporting bias, and other bias (Higgins et al., 2011). These two tools can complement each other for an even better quality assessment of physiotherapy research and help disseminate and make transparent and available research output, encouraging interdisciplinary research along the way.
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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 | 0.684 | 0.899 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.025 | 0.028 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".