Financial Toxicity and Breast Cancer: Why Does It Matter, Who Is at Risk, and How Do We Intervene?
Bibliographic record
Abstract
Financial toxicity, or the financial burden patients experience because of medical costs, can lead to negative patient effects including lower quality of life, compromised clinical care, and worse health outcomes. People with cancer and survivors are more likely to have financial toxicity than those without cancer, and patients with breast cancer are uniquely at risk. Patients with breast cancer often require multimodal treatment (surgery, radiation, and/or systemic therapy) and adjuvant hormonal therapy can continue for years after primary treatment. With improved disease outcomes, patients with breast cancer have prolonged survivorship often lasting decades but may carry chronic toxicities from treatment; both ongoing treatment of metastatic disease and long-term surveillance include continued tests, imaging, and medical visits that add to the cumulative burden on patients and their families. Additionally, breast cancer predominately affects women, who are more likely to have dual caregiver responsibilities, and increasingly is diagnosed in younger patients, who may have fertility preservation expenses and are more likely to experience education and/or employment disruption. When faced with high costs, patients may face difficult decisions regarding what sacrifices they are willing to endure to receive care. Interventions designed to reduce financial toxicity are moving out of the pilot phase, and ongoing randomized trials are expected to provide evidence into the effectiveness of financial navigation programs. Further work to address financial toxicity in breast cancer at the patient-provider, institutional, and governmental levels is needed for comprehensively better financial outcomes and quality of life.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".