Network meta-analysis of eribulin versus other chemotherapies used as second- or later-line treatment in locally advanced or metastatic breast cancer
Bibliographic record
Abstract
BACKGROUND: Eribulin mesylate (ERI; Halaven®) is a microtubule inhibitor approved in the United States for metastatic breast cancer patients with at least two prior chemotherapy regimens for metastatic breast cancer, and in the European Union in locally advanced breast cancer or metastatic breast cancer patients who progressed after at least one chemotherapy for advanced disease. This network meta-analysis compared the efficacy and safety of ERI versus other chemotherapies in this setting. METHODS: Systematic searches conducted in MEDLINE, Embase, and the Cochrane Central Register of Clinical Trials identified randomized controlled trials of locally advanced breast cancer/metastatic breast cancer chemotherapies in second- or later-line settings. Efficacy assessment included pre-specified subgroup analysis of breast cancer subtypes. Included studies were assessed for quality using the Centre for Reviews and Dissemination tool. Bayesian network meta-analysis estimated primary outcomes of overall survival and progression-free survival using fixed-effect models. Comparators included: capecitabine (CAP), gemcitabine (GEM), ixabepilone (IXA), utidelone (UTI), treatment by physician's choice (TPC), and vinorelbine (VIN). RESULTS: The network meta-analysis included seven trials. Results showed that second- or later-line patients treated with ERI had statistically longer overall survival versus TPC (hazard ratio [HR]: 0.81; credible interval [CrI]: 0.66-0.99) or GEM+VIN (0.62; 0.42-0.90) and statistically longer progression-free survival versus TPC (0.76; 0.64-0.90), but statistically shorter progression-free survival versus CAP+IXA (1.40; 1.17-1.67) and CAP+UTI (1.61; 1.23-2.12). In triple negative breast cancer, ERI had statistically longer overall survival versus CAP (0.70; 0.54-0.90); no statistical differences in progression-free survival were observed in triple negative breast cancer. CONCLUSIONS: This network meta-analysis suggests that ERI may provide an overall survival benefit in the overall locally advanced breast cancer/metastatic breast cancer populations and triple negative breast cancer subgroup compared to standard treatments. These findings support the use of ERI in second- or later-line treatment of patients with locally advanced breast cancer/metastatic breast cancer.
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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 | 0.030 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.060 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".