Results from a focus group evaluating TrialTALK, a designed conversation to facilitate cancer treatment shared decision making.
Bibliographic record
Abstract
34 Background: Cancer treatment decision making involves timely, high-risk, shared decision-making conversations between patients, their families, and their oncologists. These conversations are a prime target for a carefully designed, easy to interpret approach to facilitate preference-sensitive decision-making. The TrialTALK approach has two core elements: a verbal approach and a pen & paper diagram. The diagram includes the diagnosis with prognostic implications, available treatment options along with estimates for efficacy and anticipated impact on daily life. The verbal conversation corresponds to the diagram and includes a phrase to encourage deliberation and empathic responses. Here, we report the results of a focus group conducted in Toronto comparing two approaches to a cancer decision making conversation. Methods: Our focus group was comprised of 9 patients, all with incurable malignancy. They observed a live reading of an actual transcript of a cancer treatment decision making conversation between a patient and oncologist who presented three options: observation, chemotherapy, and a clinical trial. The investigators re-organized the conversation, reusing as many words as possible, into the TrialTALK framework. No new information was introduced. After watching each scene, participants discussed and rated the conversation for information needs and indicated the decision. Results: Three (33%) said rated the standard conversation as meeting their informational needs while 7/9 (78%) reported needs were met by the designed approach. Decision making preferences following the conversations are shown in the table. Patients reported feeling the physician in the designed conversation was more prepared; they felt greater trust and confidence; they valued the paper diagram; they felt more empowered and engaged in the decision. Conclusions: A designed conversation may improve patient understanding, influence decision making, while also enhancing the patient-physician relationship. Clinical trial information: NCT03656276. [Table: see text]
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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 | 0.031 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".