Do a Clinical Practice Guideline Facilitate Shared Decision‐Making? Development of a French Assessment Tool Using the Delphi Consensus Method
Bibliographic record
Abstract
Background: Evidence‐based medicine (EBM) is a prime component of medical practice. EBM often translate into clinical practice guidelines (CPG) widely used by healthcare providers. However, CPGs are often focused on a specific pathology, and they rarely make a room for shared decision‐making (SDM) another key dimension, centered on the information exchange between the physician and the patient, the deliberation/discussion, and the decision made based on a common agreement. An assessment tool is therefore needed to determine whether the structure of CPGs allows or not the integration of SDM. Objectives: To develop an assessment tool in French that could quantify the degree to which CPG facilitate SDM by translating and adapting the elements developed in international consensus studies. Method: A Delphi consensus method including seven experts selected from the leading scientific community on the topic. Consensus was considered to have been reached when the approval rate reached 70%. Results: A consensus for the adaptation, relevance, and adjustment of 19 strategies was reached after three rounds. Based on these strategies, 17 criteria were developed. They 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,” and recommendation‐specific strategies such as giving to the patient a copy of his/her personalized treatment plan, recommending which patient decision aid could be used and when, or encouraging the patient to exchange with close relatives and friends for the discussion. Conclusion: We developed a 17‐item tool to assess whether or not a CPG facilitates sustainable development. This tool will have to be tested to ensure that it is easy to use, relevant and reproducible, and thus meets the expected quality criteria. Such a tool would enable researchers and patients alike to assess CPGs using a common benchmark, would support national and international benchmarking processes, and provide a starting point for future improvement. Translations into other languages could broaden the scope of use.
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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 | 0.267 | 0.329 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".