Moving From Evidence To Decisions in Guidelines: An Analysis of Guidance Documents
Bibliographic record
Abstract
Abstract Background The process of moving from evidence to decisions (EtD) represents a cornerstone within guideline development methods. Little is known about the processes used by organizations in charge of guideline development and what criteria they consider when formulating recommendations. Objective To identify and describe the processes suggested for the formulation of healthcare recommendations in health care guidelines available in guidance documents. Methods We searched in spring 2020 the Guidelines International Network (G-I-N) website, MEDLINE, and The Cochrane Methodology Register to retrieve guidance documents published in the last decade by organizations dedicated to guideline development. Pairs of researchers independently selected and extracted data about the characteristics of the guidance document, including explicit or implicit recommendation-related criteria and processes considered, as well as the use of frameworks. We conducted both descriptive and bivariate analyses. Results We included 68 guidance documents, published mostly by scientific societies (58%). Most of the organizations reported a system for grading the strength of recommendations (88%), half of them being the GRADE approach. Two out of three guidance documents (66%) proposed the use of a framework to guide the EtD process. We identified 14 recommendation-related criteria. The GRADE Evidence to Decision (GRADE-EtD) framework was the most often reported framework (19 organizations, 42%), whereas 20 organizations (44%) proposed their own multi-criteria frameworks. Using any EtD framework was related with a more comprehensive set of recommendation-related criteria compared to no framework, especially for criteria like values, equity, and acceptability. A similar association was observed between the GRADE-EtD framework and either no framework or another EtD frameworks. Conclusion The use of systematic and structured processes for moving from evidence to decisions is still limited among international organizations. The use of EtD frameworks facilitates the inclusion of relevant recommendation criteria. Among the structured frameworks, the GRADE-EtD framework offers the most comprehensive perspective for evidence-informed decision-making processes. More complete and detailed reporting in the guidance documents is warranted.
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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 | 0.196 | 0.732 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.052 | 0.067 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| 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; 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".