Financial Toxicity and Out-of-Pocket Costs for Patients with Head and Neck Cancer
Bibliographic record
Abstract
Aim: To quantify financial toxicity and out-of-pocket costs for patients with HNC in Australia and explore their relationship with health-related quality of life (HRQoL). Methods: A cross-sectional survey was administered to patients with HNC 1–3 years after radiotherapy at a regional hospital in Australia. The survey included questions on sociodemographics, out-of-pocket expenses, HRQoL, and the Financial Index of Toxicity (FIT) tool. The relationship between high financial toxicity scores (top quartile) and HRQoL was explored. Results: Of the 57 participants included in the study, 41 (72%) reported out-of-pocket expenses at a median of AUD 1796 (IQR AUD 2700) and a maximum of AUD 25,050. The median FIT score was 13.9 (IQR 19.5) and patients with high financial toxicity (n = 14) reported poorer HRQoL (76.5 vs. 114.5, p < 0.001). Patients who were not married had higher FIT scores (23.1 vs. 11.1, p = 0.01), as did those with lower education (19.3 vs. 11.1, p = 0.06). Participants with private health insurance had lower financial toxicity scores (8.3 vs. 17.6, p = 0.01). Medications (41%, median AUD 400), dietary supplements (41%, median AUD 600), travel (36%, median AUD 525), and dental (29%, AUD 388) were the most common out-of-pocket expenses. Participants living in rural locations (≥100 km from the hospital) had higher out-of-pocket expenses (AUD 2655 vs. AUD 730, p = 0.01). Conclusion: Financial toxicity is associated with poorer HRQoL for many patients with HNC following treatment. Further research is needed to investigate interventions aimed at reducing financial toxicity and how these can best be incorporated into routine clinical care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".