A comparison of the PEDro and Downs and Black quality assessment tools using the acquired brain injury intervention literature
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to examine the correlation between the Physiotherapy Evidence Database (PEDro) and the Downs and Black (D&B) quality assessment scale and the PEDro and a modified D&B assessment scores in a research synthesis of the ABI literature. METHODS AND MAIN OUTCOMES: A systematic review of the literature from 1980-2007 was conducted looking at treatment interventions following an ABI published in peer-reviewed English language journals. Of the articles chosen for inclusion in the study, 165 were identified as randomised controlled trials (RCT). All RCTs were scored using two quality assessment tools: the PEDro and D&B quality assessment scales. Items from these two scales were compared to identify which questions addressed similar information. RESULTS: The association between the overall PEDro and D&B scores was moderately high (r = 0.71, p < 0.01) indicating a significant relationship between these two quality assessment tools. When considering the modified D&B scores, which contained a subset of questions deemed most comparable to the PEDro scale, the correlation between the two was also moderately high (r = 0.68, p < 0.01). CONCLUSIONS: Further analysis is required to investigate the strength of the relationship between these two scales in the assessment of RCTs.
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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 | 0.183 | 0.382 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.030 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".