The GRADE evidence-to-decision framework: a report of its testing and application in 15 international guideline panels
Bibliographic record
Abstract
Judgments underlying guideline recommendations are seldom recorded and presented in a systematic fashion. The GRADE Evidence-to-Decision Framework (EtD) offers a transparent way to record and report guideline developers' judgments. In this paper, we report the experiences with the EtD frameworks in 15 real guideline panels. Following the guideline panel meetings, we asked methodologists participating in the panel to provide feedback regarding the EtD framework. They were instructed to consider their own experience and the feedback collected from the rest of the panel. Two investigators independently summarized the responses and jointly interpreted the data using pre-specified domains as coding system. We asked methodologists to review the results and provide further input to improve the structure of the EtDs iteratively. The EtD framework was well received, and the comments were generally positive. Methodologists felt that in a real guideline panel, the EtD framework helps structuring a complex process through relatively simple steps in an explicit and transparent way. However, some sections (e.g., "values and preferences" and "balance between benefits and harms") required further development and clarification that were considered in the current version of the EtD framework. The use of an EtD framework in guideline development offers a structured and explicit way to record and report the judgments and discussion of guideline panels during the formulation of recommendations. In addition, it facilitates the formulation of recommendations, assessment of their strength, and identifying gaps in research.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".