Correlation Between Financial Toxicity, Quality of Life, and Patient Satisfaction in an Insured Population of Breast Cancer Surgical Patients: A Single-Institution Retrospective Study
Bibliographic record
Abstract
BACKGROUND: The relationship between treatment-related, cost-associated distress "financial toxicity" (FT) and quality-of life (QOL) in breast cancer patients remains poorly characterized. This study leverages validated patient-reported outcomes measures (PROMs) to analyze the association between FT and QOL and satisfaction among women undergoing ablative breast cancer surgery. STUDY DESIGN: This is a single-institution cross-sectional survey of all female breast cancer patients (>18 years old) who underwent lumpectomy or mastectomy between January 2018 and June 2019. FT was measured via the 11-item COmprehensive Score for financial Toxicity (COST) instrument. The BREAST-Q and SF-12 were used to asses condition-specific and global QOL, respectively. Responses were linked with demographic and clinical data. Pearson correlation coefficient and multivariable regression were used to examine associations. RESULTS: Our analytical sample consisted of 532 patients; mean age 58, mostly white (76.7%), employed (63.7%), married/committed (73.7%), with 64.3% undergoing reconstruction. Median household income was $80,000 to $120,000/year, and mean COST score was 28.0. After multivariable adjustment, a positive relationship for all outcomes was noted; lower COST (greater cost-associated distress) was associated with lower BREAST-Q and SF-12 scores. This relationship was strongest for BREAST-Q psychosocial well-being, for which we observed a 0.89 (95% CI 0.76-1.03) change per unit change in COST score. CONCLUSIONS: Financial toxicity captured in this study correlates with statistically significant and clinically important differences in BREAST-Q psychosocial well-being, patient satisfaction with reconstructed breasts, and SF-12 global mental and physical quality of life. Treatment costs should be included in the shared decision-making for breast cancer surgery. Future prospective outcomes research should integrate COST.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".