Investigating how the GRADE Evidence to Decision (EtD) framework is used in Clinical Guidelines: a scoping review protocol
Bibliographic record
Abstract
<ns3:p> <ns3:bold>Introduction:</ns3:bold> The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) evidence to decision (EtD) framework provides a structured and transparent approach for clinical guideline developers to use when formulating recommendations. Understanding how stakeholders use the EtD framework will inform how best to provide future training and support. This scoping review objective is to identify the key characteristics of how the GRADE EtD framework is used and identify studies on perception of use by those involved in developing clinical guidelines. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> JBI methodology for scoping reviews will be followed. This scoping review will consider both peer review published literature and grey literature. This will include empirical studies on the use of EtDs (including both quantitative, qualitative, and mixed methods primary research articles) and discussion papers/ commentaries on the experience of using the EtD. It will also include a random sample of publicly available populated EtDs identified from databases and repositories of GRADE guidelines. The search strategy will aim to locate both published and unpublished documents. First, we will conduct an exploratory search of MEDLINE and Embase (Elsevier), supplemented with citation analysis of included articles. Populated EtDs will be identified through searches of databases and repositories of GRADE guidelines. Two researchers will independently screen, select, and extract identified documents. Data will be presented in tables and summarized descriptively. </ns3:p> <ns3:p> <ns3:bold>Conclusion:</ns3:bold> This scoping review will identify the key characteristics of how the GRADE EtD framework is currently being used in clinical guidelines. Review findings can be used to inform future guidance and requirements for using GRADE EtD, as well as training on how to consider the criteria in developing recommendations. Results will be disseminated through publications in peer – reviewed journals and conference presentations. We will present our findings to relevant stakeholders via the networks of the co-author team at a one-day workshop. </ns3:p>
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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 | 0.372 | 0.459 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.013 |
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; both teacher heads 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".