064WS How to Use the GRADE ”Evidence-to-Recommendations Framework” to Develop Guideline Recommendations for Therapeutic Interventions
Bibliographic record
Abstract
Background Moving from evidence to recommendations in guideline development requires balancing evidence quality with the benefits and harms of therapeutic interventions, patient preferences, and resource and cost considerations. The GRADE Working Group has developed an approach to integrate these factors into development of clinical practice recommendations that is currently further defined in the DECIDE (Developing and Evaluating Communication Strategies to Support Informed Decisions and Practice Based on Evidence) project. Objectives/Goal To train guideline developers and those working with guideline panels to facilitate the decision-making process for development of recommendations for therapeutic interventions using the GRADE “Evidence-to-Recommendations Framework.” Target Audience Guideline developers, especially those working with guideline panels to develop recommendations for clinical practice. Description of the Workshop and of the Methods used to Facilitate Interactions An overview of the GRADE “Evidence-to-Recommendations Framework” will be followed by facilitated small group work to develop guideline recommendations. Participants will work together in a simulated guideline panel, and be asked to develop guideline recommendations taking into consideration the quality of evidence from a GRADE evidence summary profile, the balance of benefits vs. harms of an intervention, patient preferences and resource implications. Facilitators will guide the small workgroups through the decision-making process using materials from recent examples of guidelines developed using the “Evidence-to-Recommendations Framework.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".