Role of patient preferences in clinical practice guidelines: a multiple methods study using guidelines from oncology as a case
Bibliographic record
Abstract
OBJECTIVE: Many treatment decisions are preference-sensitive and call for shared decision-making, notably when benefits are limited or uncertain, and harms impact quality of life. We explored if clinical practice guidelines (CPGs) acknowledge preference-sensitive decisions in how they motivate and phrase their recommendations. DESIGN: We performed a qualitative analysis of the content of CPGs and verified the results in semistructured interviews with CPG panel members. SETTING: Dutch oncology CPGs issued in 2010 or later, concerning primary treatment with curative intent. PARTICIPANTS: 14 CPG panel members. MAIN OUTCOMES: For treatment recommendations from six CPG modules, two researchers extracted the following: strength of recommendation in terms of the Grading of Recommendations Assessment, Development and Evaluation and its consistency with the CPG text; completeness of presentation of benefits and harms; incorporation of patient preferences; statements on the panel's benefits-harm trade-off underlying recommendation; and advice on patient involvement in decision-making. RESULTS: We identified 32 recommendations, 18 were acknowledged preference-sensitive decisions. Three of 14 strong recommendations should have been weak based on the module text. The reporting of benefits and harms, and their probabilities, was sufficiently complete and clear to inform the strength of the recommendation in one of the six modules only. Numerical probabilities were seldom presented. None of the modules presented information on patient preferences. CPG panel's preferences were not made explicit, but appeared to have impacted 15 of 32 recommendations. Advice to involve patients and their preferences in decision-making was given for 20 recommendations (14 weak). Interviewees confirmed these findings. Explanations for lack of information were, for example, that clinicians know the information and that CPGs must be short. Explanations for trade-offs made were cultural-historical preferences, compliance with daily care, presumed role of CPGs and lack of time. CONCLUSIONS: The motivation and phrasing of CPG recommendations do not stimulate choice awareness and a neutral presentation of options, thus hindering shared decision-making.
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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 | 0.063 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".