Translating Evidence to Facilitate Shared Decision Making: Development and Usability of a Consult Decision Aid Prototype
Bibliographic record
Abstract
AIM: The purpose of this study was to translate evidence from Cochrane Reviews into a format that can be used to facilitate shared decision making during the consultation, namely patient decision aids. METHODS: A systematic development process (a) established a stakeholder committee; (b) developed a prototype according to the International Patient Decision Aid Standards; (c) applied the prototype to a Cochrane Review and used an interview-guided survey to evaluate acceptability/usability; (d) created 12 consult decision aids; and (e) used a Delphi process to reach consensus on considerations for creating a consult decision aid. RESULTS: The 1-page prototype includes (a) a title specifying the decision; (b) information on the health condition, options, benefits/harms with probabilities; (c) an explicit values clarification exercise; and (d) questions to screen for decisional conflict. Hyperlinks provide additional information on definitions, probabilities presented graphically, and references. Fourteen Cochrane Consumer Network members and Cochrane Editorial Unit staff participated. Thirteen reported that it would help patient/clinician discussions and were willing to use and/or recommend it. Seven indicated the right amount of information, six not enough, and one too much. Changes to the prototype were more links to definitions, more white space, and details on GRADE evidence ratings. Creating 12 consult decision aids took about 4 h each. We identified ten considerations when selecting Cochrane Reviews for creating consult decision aids. CONCLUSIONS: Using a systematic process, we developed a consult decision aid prototype to be populated with evidence from Cochrane Reviews. It was acceptable and easy to apply. Future studies will evaluate implementation of consult decision aids.
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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 | 0.091 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".