The Role of Disease Label in Patient Perceptions and Treatment Decisions in the Setting of Low-Risk Malignant Neoplasms
Bibliographic record
Abstract
Importance: The cancer disease label may lead to overtreatment of low-risk malignant neoplasms owing to a patient's emotional response or misunderstanding of prognosis. Decision making should be driven by risks and benefits of treatment and prognosis rather than disease label. Objective: To determine whether disease label plays a role in patient decision making in the setting of low-risk malignant neoplasms and to determine how the magnitude of the disease-label effect compares with preferences for treatment and prognosis. Design, Setting, and Participants: A discrete choice experiment conducted using an online survey of 1314 US residents in which participants indicated their preferences between a series of 2 hypothetical vignettes describing the incidental discovery of a small thyroid lesion. Vignettes varied on 3 attributes: disease label (cancer, tumor, or nodule); treatment (active surveillance or hemithyroidectomy); and risk of progression or recurrence (0%, 1%, 2%, or 5%). The independent associations of each attribute with likelihood of vignette selection was estimated with a Bayesian mixed logit model. Main Outcomes and Measures: The preference weight of the cancer disease label was compared with preference weights for other attributes. Results: In 1068 predominantly healthy respondents (605 women and 463 men) with a median age of 35 years (range, 18-78 years), the cancer disease label played a considerable role in respondent decision making independent of treatment offered and risk of progression or recurrence. Participants accepted a 4-percentage-point increase in risk of progression or recurrence (from 1% to 5%) to avoid labeling their disease as cancer in favor of nodule (marginal rate of substitution [MRS], 1.0; 95% credible interval [CrI], 0.9-1.1). Preference for the nodule label instead of cancer was similar in magnitude to the preference for active surveillance over surgery (MRS, 1.0; 95% CrI, 0.9-1.1). Conclusions and Relevance: Disease label plays a role in patient preference independent of treatment risks or prognosis. Raising the threshold for biopsy or removing the word cancer from the disease label may mitigate patient preference for aggressive treatment of low-risk lesions. Health care professionals should emphasize treatment risks and benefits and natural disease history when supporting treatment decisions for potentially innocuous epithelial malignant neoplasms.
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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 | 0.000 |
| 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".