Construct validity of the Physiotherapy Evidence Database (PEDro) quality scale for randomized trials: Item response theory and factor analyses
Bibliographic record
Abstract
BACKGROUND: There is an agreement that the methodological quality of randomized trials should be assessed in systematic reviews, but there is a debate on how this should be done. We conducted a construct validation study of the Physiotherapy Evidence Database (PEDro) scale, which is widely used to assess the quality of trials in physical therapy and rehabilitation. METHODS: We analyzed 345 trials that were included in Cochrane reviews and for which a PEDro summary score was available. We used one- and two-parameter logistic item response theory (IRT) models to study the psychometric properties of the PEDro scale and assessed the items' difficulty and discrimination parameters. We ran goodness of fit post estimations and examined the IRT unidimensionality assumption with a multidimensional IRT (MIRT) model. RESULTS: Out of a maximum of 10, the mean PEDro summary score was 5.46 (SD = 1.51). The allocation concealment and intention-to-treat scale items contributed most of the information on the underlying construct (with discriminations of 1.79 and 2.05, respectively) at similar difficulties (0.63 and 0.65, respectively). The other items provided little additional information and did not distinguish trials of different quality. There was substantial evidence of departure from the unidimensionality assumption, suggesting that the PEDro items relate to more than one latent trait. CONCLUSIONS: Our findings question the construct validity of the PEDro scale to assess the methodological quality of clinical trials. PEDro summary scores should not be used; rather, the physiotherapy community should consider working with the individual items of the scale.
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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 | 0.533 | 0.770 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.018 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".