Evidence to decision frameworks enabled structured and explicit development of healthcare recommendations
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to identify and describe the processes suggested for the formulation of healthcare recommendations in healthcare guidelines available in guidance documents. METHODS: We searched international databases in May 2020 to retrieve guidance documents published 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 evidence to decision (EtD) frameworks. RESULTS: We included 68 guidance documents. Most organizations reported a system for grading the strength of recommendations (88%), half of them being the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach. Two out of three guidance documents (66%) proposed the use of a framework to guide the EtD process. The GRADE-EtD framework was the most often reported framework (19 organizations, 42%), whereas 20 organizations (44%) proposed their own multicriteria 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. CONCLUSION: Although limited, the use of EtD frameworks was associated with the inclusion of relevant recommendation criteria. Among the EtD structured frameworks, the GRADE-EtD framework offers the most comprehensive perspective for evidence-informed decision-making processes.
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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 | 0.177 | 0.434 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".