A framework for practical issues was developed to inform shared decision-making tools and clinical guidelines
Bibliographic record
Abstract
OBJECTIVES: The objective of the study was to develop and test feasibility of a framework of patient-important practical issues. STUDY DESIGN AND SETTING: Guidelines and shared decision-making tools help facilitate discussions about patient-important outcomes of care alternatives, but typically ignore practical issues patients consider when implementing care into their daily routines. Using grounded theory, practical issues in the HealthTalk.org registry and in Option Grids were identified and categorized into a framework. We integrated the framework into the MAGIC authoring and publication platform and digitally structured authoring and publication platform and appraised its use in The BMJ Rapid Recommendations. RESULTS: The framework included the following 15 categories: medication routine, tests and visits, procedure and device, recovery and adaptation, coordination of care, adverse effects, interactions and antidote, physical well-being, emotional well-being, pregnancy and nursing, costs and access, food and drinks, exercise and activities, social life and relationships, work and education, travel and driving. Implementation in 15 BMJ Rapid Recommendations added 283 issues to 35 recommendations. The most frequently used category was procedure and device, and the least frequent was social life and relationship. CONCLUSION: Adding practical issues systematically to evidence summaries is feasible and can inform guidelines and tools for shared decision-making. How this inclusion can improve patient-centered care remains to be determined.
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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 | 0.173 | 0.210 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 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".