Measuring financial toxicity incurred after treatment of head and neck cancer: Development and validation of the Financial Index of Toxicity questionnaire
Bibliographic record
Abstract
BACKGROUND: The treatment of head and neck cancer (HNC) may cause significant financial toxicity to patients. Herein, the authors have presented the development and validation of the Financial Index of Toxicity (FIT) instrument. METHODS: Items were generated using literature review and were based on expert opinion. In item reduction, items with factor loadings of a magnitude <0.3 in exploratory factor analysis and inverse correlations (r < 0) in test-retest analysis were eliminated. Retained items constituted the FIT. Reliability tests included internal consistency (Cronbach α) and test-retest reliability (intraclass correlation). Validity was tested using the Spearman rho by comparing FIT scores with baseline income, posttreatment lost income, and the Financial Concerns subscale of the Social Difficulties Inventory. Responsiveness analysis compared change in income and change in FIT between 12 and 24 months. RESULTS: A total of 14 items were generated and subsequently reduced to 9 items comprising 3 domains identified on exploratory factor analysis: financial stress, financial strain, and lost productivity. The FIT was administered to 430 patients with HNC at 12 to 24 months after treatment. Internal consistency was good (α = .77). Test-retest reliability was satisfactory (intraclass correlation, 0.70). Concurrent validation demonstrated mild to strong correlations between the FIT and Social Difficulties Inventory Money Matters subscale (Spearman rho, 0.26-0.61; P < .05). FIT scores were found to be inversely correlated with baseline household income (Spearman rho, -0.34; P < .001) and positively correlated with lost income (Spearman rho, 0.24; P < .001). Change in income was negatively correlated with change in FIT over time (Spearman rho, -0.25; P = .04). CONCLUSIONS: The 9-item FIT demonstrated internal and test-retest reliability as well as concurrent and construct validity. Prospective testing in patients with HNC who were treated at other facilities is needed to further establish its responsiveness and generalizability.
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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 | 0.008 | 0.011 |
| 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 | 0.001 | 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".