Developing trustworthy recommendations as part of an urgent response (1–2 weeks): a GRADE concept paper
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: The aim of this study is to propose an approach for developing trustworthy recommendations as part of urgent responses (1-2 week) in the clinical, public health, and health systems fields. STUDY DESIGN AND SETTING: We conducted a review of the literature, outlined a draft approach, refined the concept through iterative discussions, a workshop by the Grading of Recommendations Assessment, Development and Evaluation Rapid Guidelines project group, and obtained feedback from the larger Grading of Recommendations Assessment, Development and Evaluation working group. RESULTS: A request for developing recommendations within 2 week is the usual trigger for an urgent response. Although the approach builds on the general principles of trustworthy guideline development, we highlight the following steps: (1) assess the level of urgency; (2) assess feasibility; (3) set up the organizational logistics; (4) specify the question(s); (5) collect the information needed; (6) assess the adequacy of identified information; (7) develop the recommendations using one of the 4 potential approaches: adopt existing recommendations, adapt existing recommendations, develop new recommendations using existing adequate systematic review, or develop new recommendations using expert panel input; and (8) consider an updating plan. CONCLUSION: An urgent response for developing recommendations requires building a cohesive, skilled, and highly motivated multidisciplinary team with the necessary clinical, scientific, and methodological expertise; adapting to shifting needs; complying with the principles of transparency; and properly managing conflicts of interest.
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.
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 | 0.037 | 0.622 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it