Lessons Learned From Shared Decision-Making With Oral Anticoagulants: Viewpoint on Suggestions for the Development of Oral Chemotherapy Decision Aids
Bibliographic record
Abstract
Oral chemotherapy is commonly prescribed, and by using decision aids (DAs), clinicians can facilitate shared decision-making (SDM) to align treatment choices with patient goals and values. Although products exist commercially, little evidence informs the development of DAs targeting the unique challenges of oral chemotherapy. To address this gap in the literature, our objective was to review DAs developed for oral anticoagulation, DA use in oncology, and patient preference surveys to guide the development of DAs for oral chemotherapy. We focused on reviewing SDM, patient preferences, and specifically the development, efficacy, and patient experience of DAs in oral anticoagulation and oncologic conditions, ultimately including conclusions and data from 30 peer-reviewed publications in our viewpoint paper. We found that effective DAs in oral anticoagulation improved knowledge, lowered decisional conflict, increased adherence, and covered a broad range of SDM elements; however, limited information on patient experience was a common shortcoming. In oncology, DAs increased knowledge and aligned decisions with the values of the patients. Ineffective oncology DAs provided general, unclear, or overly optimistic information, while providing "too much" information was not shown to do harm. Patients preferred DAs that included pros and cons, side effects, questions to ask, and expected quality of life changes. In developing DAs for oral chemotherapy, patients should be included in the development process, and DA content should be specifically tailored to patient preferences. Providing DAs ahead of appointments proved more effective than during, and additional considerations included addressing barriers to efficacy. There is a need for evidence-based DAs to facilitate SDM for patients considering oral chemotherapy. Developers should use data from studies in oral anticoagulation, oncology, and preference surveys to optimize SDM.
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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 | 0.079 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".