Assessment of the quality of recommendations from 161 clinical practice guidelines using the Appraisal of Guidelines for Research and Evaluation–Recommendations Excellence (AGREE-REX) instrument shows there is room for improvement
Bibliographic record
Abstract
OBJECTIVE: To assess the quality of recommendations from 161 clinical practice guidelines (CPGs) using AGREE-REX-D (Appraisal of Guidelines REsearch and Evaluation-Recommendations Excellence Draft). DESIGN: Cross-sectional study SETTING: International CPG community. PARTICIPANTS: Three hundred twenty-two international CPG developers, users, and researchers. INTERVENTION: Participants were assigned to appraise one of 161 CPGs selected for the study using the AGREE-REX-D tool MAIN OUTCOME MEASURES: AGREE-REX-D scores of 161 CPGs (7-point scale, maximum 7). RESULTS: Recommendations from 161 CPGs were appraised by 322 participants using the AGREE-REX-D. CPGs were developed by 67 different organizations. The total overall average score of the CPG recommendations was 4.23 (standard deviation (SD) = 1.14). AGREE-REX-D items that scored the highest were (mean; SD): evidence (5.51; 1.14), clinical relevance (5.95; SD 0.8), and patients/population relevance (4.87; SD 1.33), while the lowest scores were observed for the policy values (3.44; SD 1.53), local applicability (3,56; SD 1.47), and resources, tools, and capacity (3.49; SD 1.44) items. CPGs developed by government-supported organizations and developed in the UK and Canada had significantly higher recommendation quality scores with the AGREE-REX-D tool (p < 0.05) than their comparators. CONCLUSIONS: We found that there is significant room for improvement of some CPGs such as the considerations of patient/population values, policy values, local applicability and resources, tools, and capacity. These findings may be considered a baseline upon which to measure future improvements in the quality of CPGs.
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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 | 0.228 | 0.411 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".