Financial Toxicity in Breast Implant–Associated Anaplastic Large Cell Lymphoma
Bibliographic record
Abstract
BACKGROUND: Financial toxicity is a growing concern due to its considerable effects on medical adherence, quality of life, and mortality. The cost associated with breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is substantial from diagnosis to treatment, including adjuvant therapy and surgery. This study aims to assess the prevalence of financial toxicity in BIA-ALCL patients. METHODS: We performed a cross-sectional, survey-based study on women with confirmed cases of BIA-ALCL from December 2019 to March 2023. The primary study outcomes were financial toxicity measured by Comprehensive Score for Financial Toxicity (COST) score and patient-reported financial burden measured by the responses to the Evaluation of the Financial Impact of BIA-ALCL survey. Lower COST scores signify higher financial toxicity. Responses were linked to patient data extracted from the medical records. RESULTS: Thirty-two women treated for confirmed BIA-ALCL were included. Patients were all White and were diagnosed at a median age of 51 years (range, 41-65 years). The mean COST score was 27.9 ± 2.23. Lower COST scores were associated with receipt of radiotherapy ( P = 0.033), exceeding credit card limits ( P = 0.036), living paycheck to paycheck ( P = 0.00027), requiring financial support from friends and family ( P = 0.00044), and instability in household finances ( P = 0.034). CONCLUSIONS: Financial toxicity is prevalent in BIA-ALCL patients and has a substantial impact on patient reported burden. Insurance denial is frequent for patients with a prior history of cosmetic augmentation. Risk assessments and cost discussions should occur throughout the care continuum to minimize financial burden.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".