Investigation of Adverse-Event-Related Costs for Patients With Metastatic Breast Cancer in a Real-World Setting
Bibliographic record
Abstract
BACKGROUND: Existing treatments for metastatic breast cancer (mBC) are often effective but can cause adverse events (AEs). This study aimed to identify AEs associated with chemotherapies commonly used in mBC treatment (phase 1) and to quantify the economic impact of these AEs (phase 2). MATERIALS AND METHODS: Patients in phase 1 had at least one claim for therapy for mBC, with at least one episode with single or multiple agents. The most common chemotherapy-related complications were identified using medical and pharmacy claims data. In phase 2, patients meeting study criteria were divided into four treatment cohorts by the line of treatment and chemotherapy received: first-line taxane-treated patients, second-line taxane-treated patients, first-line capecitabine-treated patients, and second-line capecitabine-treated patients. Average monthly AE-related health care costs per cohort were stratified by cost component. Total monthly costs per number of AEs were also calculated. RESULTS: On average, patients in phase 1 (n = 1,551) had 2 episodes of treatment, with a mean duration of 131 days. The most frequently noted complications were anemia (50.7% of mBC treatment episodes), bilirubin elevation (26.4%), and leukopenia (24.8%). In phase 2, costs related to AEs were primarily driven by incremental inpatient, outpatient, and pharmacy costs. Increases in average monthly costs ranged from $854 (9.0%) to $5,320 (69.5%), according to cohort. Overall costs increased with increasing numbers of AEs. CONCLUSION: Chemotherapy-related AEs in patients with mBC are associated with a substantial economic burden that increases with the number of AEs reported.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".