Advancing guideline quality through country-wide and regional quality assessment of CPGs using AGREE: a scoping review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Clinical practice guidelines (CPGs) are evaluated for quality with the Appraisal of Guidelines for Research and Evaluation (AGREE) tool, and this is increasingly done for different countries and regional groupings. This scoping review aimed to describe, map, and compare these geographical synthesis studies, that assessed CPG quality using the AGREE tool. This allowed a global interpretation of the current landscape of these country-wide or regional synthesis studies, and a closer look at its methodology and results. STUDY DESIGN AND METHODS: A scoping review was conducted searching databases Medline, Embase, Epistemonikos, and grey literature on 5 October 2021 for synthesis studies using the later versions of AGREE (AGREE II, AGREE-REX and AGREE GRS) to evaluate country-wide or regional CPG quality. Country-wide or regional synthesis studies were the units of analysis, and simple descriptive statistics was used to conduct the analysis. AGREE scores were analysed across subgroups into one of the seven Sustainable Development Goal regions, to allow for meaningful interpretation. RESULTS: Fifty-seven studies fulfilled our eligibility criteria, which had included a total of 2918 CPGs. Regions of the Global North, and Eastern and South-Eastern Asia were most represented. Studies were consistent in reporting and presenting their AGREE domain and overall results, but only 18% (n = 10) reported development methods, and 19% (n = 11) reported use of Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Overall scores for domains Rigor of development and Editorial independence were low, notably in middle-income countries. Editorial Independence scores, especially, were low across all regions with a maximum domain score of 46%. There were no studies from low-income countries. CONCLUSION: There is an increasing tendency to appraise country-wide and regionally grouped CPGs, using quality appraisal tools. The AGREE tool, evaluated in this scoping review, was used well and consistently across studies. Findings of low report rates of development of CPGs and of use of GRADE is concerning, as is low domain scores globally for Editorial Independence. Transparent reporting of funding and competing interests, as well as highlighting evidence-to-decision processes, should assist in further improving CPG quality as clinicians are in dire need of high-quality guidelines.
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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 | 0.384 | 0.584 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.047 | 0.046 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".