Financial toxicity in thyroid cancer: An analysis from the North American Thyroid Cancer Survivorship study.
Bibliographic record
Abstract
17 Background: Financial toxicity (FTox) has been associated with worse health-related quality-of-life (HRQoL), compliance, and even survival in cancer patients (pts). Measuring FTox and understanding its predictors are of paramount importance when planning intervention strategies, the value of care, and healthcare policies. We report FTox and its predictors in a large cohort of thyroid cancer pts and survivors. Methods: Pts with thyroid cancer were surveyed in the North American Thyroid Cancer Survivorship Study. FTox was assessed by the previously validated COmprehensive Score for financial Toxicity (COST), as well as by questions related to financial distress (out-of-pocket costs, loss of income and bankruptcy). Data on sociodemographics, income, type of disease, length of diagnosis (LOD), and prior therapies were collected. Predictors of FTox were assessed in multivariate analyses, controlling for potential confounders, such as HRQoL (as measured by the thyroid cancer-specific City of Hope instrument), type of treatment received, and LOD. Results: 591 pts with thyroid cancer within the past 6 years were surveyed in 2 countries: 553 (93.5%) in the United States (U.S.), and 38 (6.5%) in Canada. Most were women (n = 518 pts, 88%). The median LOD was 857 days (range 105-2176 days), and 430 pts (72.8%) had papillary thyroid cancer. There were 61 pts (10.3%) with Stage IV, and 11 (1.9%) were on tyrosine kinase inhibitors. Overall, 234 pts (39.5%) stated that their out-of-pocket costs were higher than previously thought; 207 pts (35%) felt their disease resulted in loss of income; 44 pts (7.4%) were unable to meet their monthly expenses; and 7 pts (1.2%) declared bankruptcy after diagnosis. The median COST value was 24 (range 0-44). In multivariate analyses, the independent predictors of worse FTox were lower income (p < 0.001), female gender (p = 0.01), lower educational level (p = 0.002), healthcare delivery in the U.S., (p = 0.002), and worse HRQoL (p < 0.001). Conclusions: A significant proportion of thyroid cancer pts experience FTox. We identified pts characteristics (gender, education, income), as well as geographical differences (healthcare delivery in the U.S.) as predictors of FTox.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".