Evaluating Patients’ Perception of the Risk of Acute Care Visits During Systemic Therapy for Cancer
Bibliographic record
Abstract
PURPOSE: Unplanned emergency department (ED) visits and hospitalizations are common during systemic cancer therapy. To determine how patients with cancer trade off treatment benefit with risk of experiencing an ED visit or hospitalization when deciding about systemic therapy, we undertook a discrete choice experiment. MATERIALS AND METHODS: Patients with breast, colorectal, or head and neck cancer contemplating, receiving, or having previously received systemic therapy were presented with 10 choice tasks (5 in the curative and 5 in the palliative setting) that varied on 3 attributes: benefit, risk of ED visit, and risk of hospitalization. Preferences for attributes and levels were measured using part-worth utilities, estimated using hierarchical Bayes analysis. Segmentation analysis was conducted to identify subgroups with different preferences. RESULTS: A total of 293 patients completed the survey; most were female (76%), had breast cancer (63%), and were currently receiving systemic therapy (72%) with curative intent (59%). Benefit was the most important decision attribute regardless of treatment intent, followed by risk of hospitalization, then risk of ED visit. Two segments were observed: one large cluster exhibiting logical and consistent choices, and a smaller segment exhibiting illogical and inconsistent choices. Patients in the latter segment were more likely to have metastatic head and neck cancer, be male, were older, and reported fewer prior ED visits. CONCLUSION: Although the risk of ED visit or hospitalization contributes to patient treatment preferences, benefit was the most important attribute. Segmentation suggests that a subset of patients may lack cognitive abilities, engagement, or literacy to consistently evaluate treatment choices. Understanding this subset may provide insight into patients' decision making and understanding of treatment options.
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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 | 0.000 | 0.002 |
| 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".