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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it