Shared Decision Making in the Care of Patients With Cancer
Bibliographic record
Abstract
Shared decision making (SDM) is a method of care that is suitable for the care of patients with cancer. It involves a collaborative conversation seeking to respond sensibly to the problematic situation of the patient, cocreating a plan of care that makes sense intellectually, practically, and emotionally. Genetic testing to identify whether a patient has a hereditary cancer syndrome represents a prime example of the importance for SDM in oncology. SDM is important for genetic testing because not only results affect current cancer treatment, cancer surveillance, and care of relatives but also these tests generate both complex results and psychological concerns. SDM conversations should take place without interruptions, disruptions, or hurry and be supported, where available, by tools that assist in conveying the relevant evidence and in supporting plan development. Examples of these tools include treatment SDM encounter aids and the Genetics Adviser. Patients are expected to play a key role in making decisions and implementing plans of care, but several evolving challenges related to the unfettered access to information and expertise of varying trustworthiness and complexity in between interactions with clinicians can both support and complicate this role. SDM should result in a plan of care that is maximally responsive to the biology and biography of each patient, maximally supportive of each patient's goals and priorities, and minimally disruptive of their lives and loves.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".