Decision Making in Oncology: A Review of Patient Decision Aids to Support Patient Participation
Bibliographic record
Abstract
Although cancer management is becoming more structured with disease-specific guidelines and clinical pathways, many decisions remain complex. Contributing to this complexity is the need to make value tradeoffs between benefits and harms across cancer treatment and/or screening options. Since there is no "best" option for everyone, decisions are defined as being of higher quality when informed with the latest scientific evidence and based on patients' informed values associated with outcomes of options. However, clinicians are not good judges of patients' values, and patients often have inadequate knowledge, unrealistic expectations, and decisional conflict that interfere with their involvement in decision making. Effective approaches to support patient involvement into clinical decisions include clinicians trained in shared decision making, question prompt sheets, patient decision aids, and decision coaching by nurses and other allied health professionals. Based on systematic review of 23 randomized trials of cancer patient decision aids, patients exposed to decision aids are more likely to participate in decision making and achieve higher-quality decisions. This review highlights key historical changes leading to patient involvement in decision making, summarizes evidence on effective interventions to support shared decision making, explores strategies to implement these interventions in oncology practices, and identifies future directions.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".