Reliability and validity of the AGREE instrument used by physical therapists in assessment of clinical practice guidelines
Bibliographic record
Abstract
BACKGROUND: The AGREE instrument has been validated for evaluating Clinical Practice Guidelines (CPG) pertaining to medical care. This study evaluated the reliability and validity of physical therapists using the AGREE to assess quality of CPGs relevant to physical therapy practice. METHODS: A total of 69 physical therapists participated and were classified as generalists, specialist or researchers. Pairs of appraisers within each category evaluated independently, a set of 6 CPG selected at random from a pool of 55 CPGs. RESULTS: Reliability between pairs of appraisers indicated low to high reliability depending on the domain and number of appraisers (0.17-0.81 for single appraiser; 0.30-0.96 when score averaged across a pair of appraisers). The highest reliability was achieved for Rigour of Development, which exceeded ICC> 0.79, if scores from pairs of appraisers were pooled. Adding more than 3 appraisers did not consistently improve reliability. Appraiser type did not determine reliability scores. End-users, including study participants and a separate sample of 102 physical therapy students, found the AGREE useful to guide critical appraisal. The construct validity of the AGREE was supported in that expected differences on Rigour of Development domains were observed between expert panels versus those with no/uncertain expertise (differences of 10-21% p = 0.09-0.001). Factor analysis with varimax rotation, produced a 4-factor solution that was similar, although not in exact agreement with the AGREE Domains. Validity was also supported by the correlation observed (Kendall-tao = 0.69) between Overall Assessment and the Rigour of Development domain. CONCLUSION: These findings suggest that the AGREE instrument is reliable and valid when used by physiotherapists to assess the quality of CPG pertaining to physical therapy health services.
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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 | 0.123 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".