The financial impact of cancer care on renal cancer patients.
Bibliographic record
Abstract
INTRODUCTION Advances in novel treatment options may render renal cell cancer (RCC) patients susceptible to the financial toxicity (FT) of cancer treatment, and the factors associated with FT are unknown. MATERIALS AND METHODS: Eligible patients were ≥ 18 years old and had a diagnosis of stage IV RCC for at least 3 months. Patients were recruited from Princess Margaret Cancer Centre and Sunnybrook Odette Cancer Centre (Toronto, Canada). FT was assessed using the validated Comprehensive Score for Financial Toxicity (COST) instrument, a 12-question survey scored from 0-44, with lower scores reflecting worse FT. Patient and treatment characteristics, out-of-pocket costs (OOP) and private insurance coverage (PIC) were collected. Factors associated with worse FT (COST score < 21) were determined. RESULTS: Sixty-five patients were approached and 80% agreed to participate (n = 52). The median age was 62 (44-88); 20% were female (n = 10); 43% were age ≥ 65 (n = 22); 63% were Caucasian (n = 31). Median COST score was 20.5 (3-44). Factors associated with worse FT were age < 65 (OR 9.5, p = 0.007), high OOP (OR 4.4, p = 0.04) and receiving treatment off clinical trial (in comparison to being on surveillance or on clinical trial) (OR 5.9, p = 0.03), when adjusting for other factors in multivariable logistic regression. However, there was no correlation between annual income or PIC and FT. CONCLUSION: Financial toxicity in the RCC population is more significant in younger patients and those on treatment outside of a clinical trial. Financial aid should be offered to these at-risk patients to optimize adherence to life prolonging RCC treatments.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".