Predictors of financial toxicity among head and neck cancer patients: A prospective cohort study.
Bibliographic record
Abstract
e18361 Background: Treatment of Head and Neck Cancer (HNC) is associated with significant costs and treatment morbidity. The impact of treatment on financial well-being has not been investigated. Methods: Patients with HNC treated at Princess Margaret Cancer Centre, Canada between 2014 and 2018 were enrolled in a longitudinal study from treatment up to 24 months of follow up. Participants completed questionnaires for demographics, out-of-pocket cost during treatment and at 3, 6, 12, 24 months, and the Financial Toxicity Index (FTI) at 12 and 24 months. The FTI is a 14 item, Likert response scale, with summary score out of 14, and higher scores indicating greater toxicity. Preliminary reliability, validity, and responsiveness for the FTI are very good and ongoing. Uni- and multi-variable analyses (UVA, MVA) were performed to identify predictors for FTI score. Results: Among 363 patients enrolled, average age was 61, 76% male, 81% Caucasian, 72% married, 84% living with others, 53% at least college educated, 57% currently unemployed and 59% had stage IV disease. Median pre-treatment household income was $85K, median lost household income was $15K over 12 months following treatment. At least some difficulty paying for food (14%), housing (17%), or medications (11%) was experienced; 13% had to borrow money and 5% had to relocate housing due to financial pressures. Predictors of greater FTI scores on MVA were younger age (p < 0.001), living alone (p = 0.009), and lower baseline household income (p < 0.001). In subgroup analysis of patients with available information, lost household income (p < 0.001) but not out of pocket costs (p = 0.58) was associated with higher FTI score. Conclusions: Financial toxicity is not uncommon in patients with HNC. Younger age, lower baseline income, living alone, and loss of household income are associated with financial toxicity. This should be a priority population for research into improved supportive care and return to work strategies.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".