Prospective Survey of Financial Toxicity Measured by the Comprehensive Score for Financial Toxicity in Japanese Patients With Cancer
Bibliographic record
Abstract
PURPOSE We previously reported on the pilot study assessing the feasibility of using the Japanese translation of the Comprehensive Score for Financial Toxicity (COST) tool to measure financial toxicity (FT) among Japanese patients with cancer. In this study, we report the results of the prospective survey assessing FT in Japanese patients with cancer using the same tool. PATIENTS AND METHODS Eligible patients were receiving chemotherapy for a solid tumor for at least 2 months. In addition to the COST survey, socioeconomic characteristics were collected by using a questionnaire and medical records. RESULTS Of the 191 patients approached, 156 (82%) responded to the questionnaire. Primary tumor sites were colorectal (n = 77; 49%), gastric (n = 39; 25%), esophageal (n = 16; 10%), thyroid (n = 9; 6%), head and neck (n = 4; 3%), and other (n = 11; 7%). Median COST score was 21 (range, 0 to 41; mean ± standard deviation, 12.1 ± 8.45), with lower COST scores indicating more severe FT. On multivariable analyses using linear regression, older age (β, 0.15 per year; 95% CI, 0.02 to 0.28; P = .02) and higher household savings (β, 8.24 per ¥15 million; 95% CI, 4.06 to 12.42; P < .001) were positively associated with COST score; nonregular employment (β, −5.37; 95% CI, −10.16 to −0.57; P = .03), retirement because of cancer (β, −5.42; 95% CI, −8.62 to −1.37; P = .009), and use of strategies to cope with the cost of cancer care (β, −5.09; 95% CI, −7.87 to −2.30; P < .001) were negatively associated with COST score. CONCLUSION Using the Japanese version of the COST tool, we identified various factors associated with FT in Japanese patients with cancer. These findings will have important implications for cancer policy planning in Japan.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".