The Association Between Financial Toxicity and Treatment Regret in Men With Localized Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Financial toxicity is emerging as an important patient-centered outcome and is understudied in prostate cancer patients. We sought to understand the association between financial burden and treatment regret in men with localized prostate cancer to better evaluate the role of financial discussions in patient counseling. METHODS: Utilizing the Comparative Effectiveness Analysis of Surgery and Radiation dataset, we identified all men accrued between 2011 and 2012 who underwent surgery, radiation, or active surveillance for localized prostate cancer. Financial burden and treatment regret were assessed at 3- and 5-year follow-up. The association between financial burden and regret was assessed using multivariable longitudinal logistic regression controlling for demographic and disease characteristics, treatment, functional outcomes, and patient expectations. RESULTS: Of the 2924 eligible patients, regret and financial burden assessments for 3- and/or 5-year follow-up were available for 81% (n = 2359). After adjustment for relevant covariates, financial burden from "finances in general" was associated with treatment regret at 3 years (odds ratio [OR] = 2.47, 95% confidence interval [CI] = 1.33 to 4.57; P = .004); however, this association was no longer statistically significant at 5-year follow-up (OR = 1.19, 95% CI = 0.56 to 2.54; P = .7). CONCLUSIONS: In this population-based sample of men with localized prostate cancer, we observed associations between financial burden and treatment regret. Our findings suggest indirect treatment costs, especially during the first 3 years after diagnosis, may impact patients more profoundly than direct costs and are important for inclusion in shared decision making.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".