Talking about treatment benefits, harms, and what matters to patients in radiation oncology: an observational study
Bibliographic record
Abstract
BACKGROUND: Shared decision making is associated with improved patient outcomes in radiation oncology. Our study aimed to capture how shared decision-making practices-namely, communicating potential harms and benefits and discussing what matters to patients-occur in usual care. METHODS: We invited a convenience sample of clinicians and patients in a radiation oncology clinic to participate in a mixed methods study. Prior to consultations, clinicians and patients completed self-administered questionnaires. We audio-recorded consultations and conducted qualitative content analysis. Patients completed a questionnaire immediately post-consultation about their recall and perceptions. RESULTS: 11 radiation oncologists, 4 residents, 14 nurses, and 40 patients (55% men; mean age 64, standard deviation or SD 9) participated. Patients had a variety of cancers; 30% had been referred for palliative radiotherapy. During consultations (mean length 45 min, SD 16), clinicians presented a median of 8 potential harms (interquartile range 6-11), using quantitative estimates 17% of the time. Patients recalled significantly fewer harms (median recall 2, interquartile range 0-3, t(38) = 9.3, p < .001). Better recall was associated with discussing potential harms with a nurse after seeing the physician (odds ratio 7.5, 95% confidence interval 1.3-67.0, p = .04.) Clinicians initiated 63% of discussions of harms and benefits while patients and families initiated 69% of discussions about values and preferences (Chi-squared(1) = 37.8, p < .001). 56% of patients reported their clinician asked what mattered to them. CONCLUSIONS: Radiation oncology clinics may wish to use interprofessional care and initiate more discussions about what matters to patients to heed Jain's (2014) reminder that, "a patient isn't a disease with a body attached but a life into which a disease has intruded."
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".