Financial toxicity amongst cancer patients and survivors: a comparative study of the United Kingdom and United States
Bibliographic record
Abstract
BACKGROUND: The study investigated the experiences of financial toxicity (FT) amongst cancer patients/survivors in the United Kingdom (UK) and the United States (USA/US). METHODS: Six hundred cancer patients/survivors residing in the UK (n = 319) or USA/US (n = 281) completed an online cross-sectional survey using the COmprehensive Score for financial Toxicity (COST)-a validated measure of FT. Severity of FT was defined as 'no' (COST scores ≥ 26), 'mild' (14-25), and 'moderate/severe' (0-13). RESULTS: Thirty-four percent of UK participants faced FT which was significantly lower compared to the USA/US at 55% (crude OR = 2.44, 95% CI 1.73-3.42). An ordered logistic regression model showed that in the USA/US, being 65 + years old (adjusted OR = 0.19, 95% CI 0.07-0.48), retired (aOR = 0.26, 95% CI 0.09-0.75), and having a higher household income (aOR ranged 0.03-0.19) decreased the risk of FT, whilst being female increased the risk (aOR = 1.83, 95% CI 1.01-3.32). In the UK, age and sex did not have an effect, but higher income and being retired showed an identical pattern compared to the US. CONCLUSIONS: FT was less prevalent and less severe in the UK, compared to the USA/US. The high prevalence of FT underscores the need to provide an additional level of protection to the most vulnerable groups than is currently offered in either country.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".