258WS Evidence to recommendations frameworks: Diagnosis
Bibliographic record
Abstract
Background Moving from evidence to recommendations (EtR) in guideline development requires balancing evidence quality with the benefits and harms of interventions, patient preferences, and resource and cost considerations. Developing recommendation about diagnostic tests and strategies is particularly challenging and requires tackling complex challenges, different than those needed for therapeutic interventions. 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. This workshop will introduce this approach and evaluate the EtR framework based on examples and hands-on exercises. Objectives To learn how to use and evaluate the EtR framework for diagnostic questions. Target Group, Suggested Audience Guideline developers, systematic reviewers, clinicians. Description of the Workshop and of the Methods used to Facilitate Interactions This workshop provides a brief didactic overview of GRADE for diagnostic questions. Each group will use a systematic review and a partially pre-filled EtR framework. During the small group work, participants will discuss challenges and advantages of the approach. Participants will then apply these concepts in small groups to develop a recommendation based on the workshop material; there will be a plenary to provide feedback that will help to enhance the work and provide opportunities for collaboration.
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 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 | 0.224 | 0.623 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.034 | 0.019 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.013 | 0.018 |
| Research integrity | 0.024 | 0.018 |
| Insufficient payload (model declined to judge) | 0.035 | 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".