282 Does a clinical practice guideline facilitate shared decision making? Development of a french assessment tool using the delphi consensus method
Bibliographic record
Abstract
Introduction Modern medical practice relies on two main concepts: evidence-based medicine (EBM), for its scientific approach, and shared decision making (SDM), that optimizes health care through the patient doctor relationship. Where clinical practice guidelines (CPG) have made their way into daily practice as an operational form of EBM, shared decision making is struggling to settle in. lt appears that CPGs do not encourage shared decision-making but no tool is currently able to verify it. Method Using the Delphi Method, we translated and converted strategies put forward in How can CPGs be adapted to facilitate SOM into a French appraisal tool that could quantify SDM in CPGs. Results Three rounds of online questionnaires enabled 7 international SDM experts from the FREeDOM collaboration to reach consensus for the translation, pertinence and adjustment of these 19 strategies into assessment criteria. The 17 criteria produced include general strategies such as adding a specific chapter on SDM, using wording that makes patient involvement explicit, presenting outcomes, benefits and harms of all options including ’doing nothing’; as well as recommendation-specific strategies such as giving the patient a copy of his individualized treatment plan, recommending which patient decision aid should be used and when, or encouraging the patient to engage a proxy for the deliberation. Conclusion By assessing whether a CPG facilitates SDM, this appraisal tool could help bridge the gap between EBM and patient-centered medicine. lt will need to be tested for ease of use, pertinence and reproducibility.
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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 | 0.203 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".