GRADE Evidence to Decision (EtD) framework: <BR>un approccio sistematico e trasparente <BR>per prendere decisioni informate in ambito sanitario. <BR>1: Introduzione
Bibliographic record
Abstract
Following the development of a unifying and transparent approach to grading the certainty of evidence and strength or recommendations, the GRADE (Grading of Recommendations Assessment, Development and Evaluation) Working Group has refined its process of moving from Evidence to Decisions. The purpose of its new Evidence to Decision (EtD) frameworks is to help people use evidence in a structured and transparent way to inform decisions in the context of clinical recommendations, coverage decisions, and health system or public health recommendations and decisions. EtD frameworks inform users about the judgments that were made and the evidence supporting those judgments by making the basis for decisions transparent to target audiences. EtD frameworks also facilitate dissemination of recommendations and enable decision makers in other jurisdictions to adopt recommendations or decisions, or adapt them to their context.
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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 | 0.296 | 0.618 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.019 |
| Bibliometrics | 0.033 | 0.019 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.014 | 0.016 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".