Does the risk of emergency department visits and hospitalizations during systemic therapy for cancer influence patients’ decisions regarding treatment?
Bibliographic record
Abstract
e18803 Background: When different treatments (tx) produce similar prognostic outcomes, other tx attributes such as toxicity may impact tx preferences. We conducted a discrete choice experiment to evaluate how patients’ value the risk of an emergency department visit (ED) or hospitalization during treatment when deciding about chemotherapy. Methods: Patients with breast, head and neck or colorectal cancer who were contemplating, receiving or had previously received systemic treatment were recruited from 2 academic cancer centres in Toronto, Ontario. Each participant completed 10 choice tasks (5 each in the adjuvant and metastatic settings, respectively) from a possible 128 combinations. Each choice task prompted them to choose between two hypothetical systemic therapies, based on 3 attributes (likelihood of benefit, risk of requiring an ED visit and risk of hospitalization during treatment) that varied across 4 levels. Data was analyzed using a multinomial logit model and individual part-worth utility (PWU) values were estimated using hierarchal Bayes routines. Results: Between 06/2015 and 09/2017, 293 patients completed the survey. Most patients were female (76%), had a diagnosis of breast cancer (63%) and were currently receiving systemic therapy (72%). 59% of patients were receiving tx with curative intent. PWU values varied as expected with higher PWUs seen for higher treatment benefit, lower risk of ED visits and lower risk of hospitalization. Treatment benefit was the most important decision attribute in both the adjuvant (59%, 95%CI 57.8-60.1%) and metastatic (67.7%, 95%CI 66.8-68.7%) scenarios, followed by risk of hospitalization (18.8 vs 22.8%) then risk of ED visits (13.5 vs 18.3%). Results were the same when stratified by patient’s own treatment intent. Conclusions: While the risks of hospitalization and ED visits contribute to patient tx preferences, the extent of tx benefit was the most important attribute regardless of treatment intent.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".