145WS Evidence-Based Guideline Development for Diagnostic Questions
Bibliographic record
Abstract
Background Developing guidelines to inform decisions regarding diagnostic tests presents unique challenges that are not encountered when addressing intervention questions. In many cases, diagnostic studies only provide test accuracy results and lack patient outcomes; outcomes that are typically sought to make recommendations. Objectives/Goal Using the lessons from our guideline group, the objectives of this workshop are for participants to learn practical skills related to the development of guidelines for diagnostic questions. Specifically, the following areas will be addressed: Generating an appropriate research question. Developing relevant eligibility criteria for choosing diagnostic studies. Critically appraising diagnostic studies using existing tools and quality criteria. Determining what types of recommendations can be generated when different types of evidence and information are available and to respond when the most relevant information is not available. Target Group, Suggested Audience Guideline developers or anyone interested in how to develop a guideline for diagnostic questions. Description of the Workshop and of the Methods used to Facilitate Interactions Using a problem-based educational approach, the workshop will begin with a quick review of the background information and objectives, and an illustrative example will be presented. Participants will then be guided through the steps of guideline development for diagnostic questions, and given problems in each step to consider and work through in small groups. Finally, participants will develop recommendations for one or two guidelines, based on evidence from diagnostic guideline projects we have completed in our guideline group.
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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 | 0.076 | 0.214 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.023 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".