A comparative effectiveness analysis of single-agent cytotoxics in triple-negative metastatic breast cancer (TN-MBC) patients.
Bibliographic record
Abstract
e17648 Background: There has been considerable progress in the treatment of MBC. However, the identification of optimal cytotoxic agents in patients with TN-MBC (negative for hormone receptors, ER/PR and HER-2) remains a therapeutic challenge. In this study, a comparative effectiveness analysis of four cytotoxic agents was conducted in patients with TN-MBC. Methods: We retrospectively identified 225 patients treated with single agent eribulin (E=47), capecitabine (C=69), gemcitabine (G=56) or vinorelbine (V=53) in 19 community oncology clinics across the U.S. Data collection included baseline patient and disease characteristics, prior therapies, performance status, duration of current therapy and dose limiting toxicities. Time to treatment failure (TTF) was measured from the first cycle of chemotherapy until disease progression, discontinuation due to toxicity, or death. TTF was then estimated using the Kaplan-Meier method and Cox proportional hazard modeling adjusted for clustering on the practice site. To control for selection bias that is inherent in observational studies, a propensity score weighted TTF analysis was also conducted. Results: Patients were comparable with respect to age, performance status, duration of disease free survival, presence of comorbidities and hemoglobin level prior to the start of chemotherapy. However, the median lines of therapy for use of C, G, V and E were second, third, third, and fourth, respectively. The median duration of treatment was approximately 2 months with C, G and E compared to 1.6 months with V. Using eribulin as the reference and adjusting for line of therapy, the propensity score weighted Cox regression analysis did not identify statistically significant differences in TTF: C vs. E: HR = 1.15 (0.75 to 1.76), G vs. E: HR = 0.62 (0.34 to 1.13), V vs. E: HR = 1.0 (0.60 to 1.37). Conclusions: In patients with TN-MBC treated in a community oncology setting, eribulin was utilized in later lines than other agents. However, eribulin demonstrated at least comparable drug activity even when used in more heavily pretreated disease relative to other agents. These findings warrant further analyses in a larger population with evaluation of biologic heterogeneity.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".