A novel framework for incorporating patient values and preferences in making guideline recommendations: guideline panel surveys
Bibliographic record
Abstract
OBJECTIVE: Universally acknowledged standards for trustworthy guidelines include the necessity to ground recommendations in patient values and preferences. When information is limited-which is typically the case-guideline panels often find it difficult to explicitly integrate patient values and preferences into their recommendations. Our objective was to develop and evaluate a framework for systematically navigating guideline panels in incorporating patient values and preferences in making recommendations. STUDY DESIGN AND SETTING: In the context of developing a guideline for colorectal cancer screening, we generated an initial framework for creating panel surveys to elicit guideline panelists' views of patient values and preferences and to inform panel discussions on recommendations. With further applications in guidelines of diverse topic areas, we dynamically refined the framework through iterative discussions and consensus. RESULTS: The finial framework consists of five steps for creating and implementing panel surveys. The surveys can serve three objectives following from the quantitative information regarding patient values and preferences that guideline panels usually require. An accompanying video provides detailed instructions of the survey. CONCLUSION: The framework for creating and implementing panel surveys offers explicit guidance for guideline panels considering transparently and systematically incorporating patient values and preferences into guideline recommendations.
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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 | 0.225 | 0.240 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".