051 Can We Automatically Produce Generic Decision Aids for the Clinical Encounter Directly from GRADE Guideline Recommendations? Experience from the Share-It Project
Bibliographic record
Abstract
Background Although decision aids (DA) can help to communicate evidence to patients, their production is time consuming, often not based on the best available evidence or rapidly outdated. Linking trustworthy guidelines and DA for shared-decision making could both overcome these limitations and enhance guideline dissemination. Objectives To test the feasibility of automatically translating any recommendations from GRADE guidelines into generic and interactive DA accessible on tablet computers for clinicians and their patients in the clinical encounter. Methods As part of the DECIDE project, we developed a framework for translating components of GRADE into DA, following the International Patient Decision Aid Standards. Using a recently published guideline, we implemented that framework in our MAGIC (Making Grade the Irresistible Choice) application – a prototype electronic guideline tool and publication platform that can automatically display recommendations in multilayered presentation formats. Results Our prototype was able to automatically translate a large number of GRADE recommendations and their supporting evidence into electronic and interactive DA. Preliminary results of user-testing in real patient-clinician interactions suggest that these DA can be used at the point of care to discuss estimates of treatment effects for patient relevant outcomes, confidence in estimates, burden of treatment, and cost issues. Discussion This study provides a proof-of-concept that components of GRADE recommendations can be interactively displayed in generic tools for interactive shared-decision making in a wide range of treatment alternatives. Implications for Guideline Developers/Users Our electronic DA offer promising opportunities to disseminate guidelines at the point of care.
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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 | 0.023 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".