Measuring financial toxicity in Australian cancer patients – Validation of the COmprehensive Score for financial Toxicity (FACT COST) measuring financial toxicity in Australian cancer patients
Bibliographic record
Abstract
AIM/BACKGROUND: The FACT COST is a patient-rated measure of financial toxicity, developed and validated in a North American population. We aimed to confirm the validity and reliability of the FACT COST in Australian cancer patients, because the Australian healthcare funding structure is different to that in North America. METHODS: A single center, cross-sectional study design investigated financial toxicity in oncology outpatients. Eligible adults had current malignancy, with or without active cancer treatment. The primary endpoint was the degree of financial toxicity experienced via the COST questionnaire; secondary endpoints included health-related quality of life (Functional Assessment of Cancer Therapy-General), anxiety, and depression (Hospital Anxiety and Depression Scale). Clinical and demographic data were recorded. Statistical analysis determined the internal consistency, test-retest reliability and validity of COST, and correlations between COST score and secondary endpoints. RESULTS: A total of 257 patients participated (79% response rate). Fifty-three percent were female; median age 63 years (range 19-88). COST scores were skewed toward less financial toxicity, median 26 (SD 10.3, range 1-43), lower scores indicating higher toxicity. High internal consistency (Cronbach's α = 0.884), test-retest reliability (ICC = 0.801), and convergent validity were demonstrated. Financial toxicity was greatest in younger participants, those with more inpatient admissions, those with a change in employment status following diagnosis, and those in the lowest income quintile. Financial toxicity was associated with worse health-related quality of life, and greater depression and anxiety. CONCLUSION: The COST measure of financial toxicity demonstrated acceptable validity parameters in an Australian outpatient population. Greater financial toxicity was associated with worse psychological well-being and with certain patient demographics.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".