Using evidence to decision frameworks led to guidelines of better quality and more credible and transparent recommendations
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To determine whether the use of Evidence to Decision (EtD) frameworks is associated to higher quality of both guidelines and individual recommendations. METHODS: We identified guidelines recently published by international organizations that have methodological guidance documents for their development. Pairs of researchers independently extracted information on the use of these frameworks, appraised the quality of the guidelines using the Appraisal of Guidelines, Research and Evaluation II Instrument (AGREE-II), and assessed the clinical credibility and implementability of the recommendations with the Appraisal of Guidelines for REsearch & Evaluation Recommendations Excellence (AGREE-REX) tool. We conducted both descriptive and inferential analyses. RESULTS: We included 66 guidelines from 17 different countries, published in the last 5 years. Thirty guidelines (45%) used an EtD framework to formulate their recommendations. Compared to those that did not use a framework, those using an EtD framework scored higher in all domains of both AGREE-II and AGREE-REX (P < 0.05). Quality scores did not differ between the use of the The Grading of Recommendations Assessment, Development and Evaluation-EtD framework (17 guidelines) or another EtD framework (13 guidelines) (P > 0.05). CONCLUSION: The use of EtD frameworks is associated with guidelines of better quality, and more credible and transparent recommendations. Endorsement of EtD frameworks by guideline developing organizations will likely increase the quality of their 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 | 0.415 | 0.783 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".