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Abstract PS5-07: Financial difficulty over time in young adults with breast cancer

2025· article· en· W4411285140 on OpenAlexaboutno aff
Sara P. Myers, Yue Zheng, Kate E. Dibble, Elizabeth A. Mittendorf, Tari A. King, Kathryn J. Ruddy, Jeffrey Peppercorn, Lidia Schapira, Virginia F. Borges, Steven E. Come, Shoshana M. Rosenberg, Ann H. Partridge

Bibliographic record

VenueClinical Cancer Research · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsBreast cancerCancerMedicineOncologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Introduction: Although young adults (YA) aged 18-39 represent the minority of breast cancer diagnoses, they are particularly vulnerable to financial hardship. Factors contributing to sustained financial hardship are incompletely understood. Arm morbidity, one such understudied factor and key source of expense, may be particularly salient for YAs given that a high proportion of this demographic presents with aggressive tumor subtypes requiring comprehensive axillary management (a known risk factor for treatment-related lymphatic injury). In this study, we leverage a multi-institutional prospective cohort of YAs to identify patterns of financial hardship over time and characterize factors associated with discrete trajectories hypothesizing that treatment-related arm morbidity would be among the factors predicting long-term financial difficulty. Methods: This analysis utilized data from women ≤ 40 years with newly diagnosed stage 0 to III breast cancer enrolled in The Young Women’s Breast Cancer Study (YWS), a multi-institutional prospective cohort study enrolling from 2006 and 2016 at Dana-Farber Cancer Institute and 12 other academic and community hospitals in the United States and Canada. Patient, disease, and treatment information was obtained from surveys serially collected through 10 years post-diagnosis. Arm morbidity was assessed by asking patients about the degree to which they experienced upper extremity swelling and/or functional limitations using two Likert-scale response items (range: 0-4). Medical record review was used to gather supplemental clinical data. The primary outcome of interest, perceived financial difficulty, was assessed serially using a single Likert-scale response item (range: 0-4) from the CAncer Rehabilitation Evaluation System (CARES) scale. Group-based trajectory modeling classified patterns of financial difficulty from baseline through 10 years post-diagnosis. Multinomial logistic regression identified patient, disease, and treatment characteristics associated with each trajectory. Results: 1008 (78%) of 1297 participants were included. Median age at diagnosis was 36 years (IQR 33-39). The majority of individuals were non-Hispanic (95%), White (88%), college graduates (83%), partnered at baseline (76%), parous (64%), and without comorbidities at enrollment (90%). Patients’ tumors were primarily stage I-II (86%), ER/PR-positive (75%), and HER2-negative (68%). Patients were more frequently treated with mastectomy than breast conservation (p<0.001). Receipt of radiation (62%), chemotherapy (75%), and endocrine therapy (63%) were common. 72% (N=727) of patients reported arm symptoms within 2 years of surgery. Three distinct financial trajectories emerged: 54% had low financial difficulty (Trajectory 1), 30% had mild difficulty that improved (Trajectory 2), and 17% had moderate/severe difficulty peaking several years after diagnosis before improving (Trajectory 3). BMI ≥ 25, undergoing bilateral mastectomy, Hispanic ethnicity, being unemployed at both baseline and 1 year, and arm symptoms were predictive of Trajectory 2 and/or 3 (more financial difficulty). Having a college degree or being partnered were predictive of Trajectory 1 (low financial difficulty). CONCLUSION: This study of YAs with breast cancer identified a subset of patients who experienced a high degree of financial difficulty that persisted into early survivorship before it improved. Targeted interventions to mitigate financial toxicity, including those focused on modifiable factors such as arm symptoms and employability/return to work after cancer, are needed. Citation Format: Sara Myers, Yue Zheng, Kate Dibble, Elizabeth A. Mittendorf, Tari A. King, Kathryn J. Ruddy, Jeffrey M. Peppercorn, Lidia Schapira, Virginia F. Borges, Steven E. Come, Shoshana M. Rosenberg, Ann H. Partridge. Financial difficulty over time in young adults with breast cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr PS5-07.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.076
GPT teacher head0.403
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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