Eribulin monotherapy improved survivals in patients with ER-positive HER2-negative metastatic breast cancer in the real world: a single institutional review
Bibliographic record
Abstract
Despite being routinely prescribed worldwide for several years, data regarding the safety, efficacy, and survival benefit of eribulin in clinical settings for the treatment of metastatic breast cancer (MBC) are limited. This retrospective observational study investigated the survival benefit of eribulin compared with conventional chemotherapy regimens in Japanese women with MBC. Women with estrogen receptor (ER)-positive human epidermal growth factor receptor 2 (HER2)-negative (ER+/HER2-) MBC, including unresectable locally advanced breast cancer, treated at a single institution were included in this study. The primary efficacy measure assessed overall survival (OS), and safety was evaluated as the number of grade 3 and 4 adverse events. Of the 293 patients analyzed, 66 received eribulin (eribulin arm) and 227 received conventional chemotherapeutic agents excluding eribulin (noneribulin arm). The median OS from MBC diagnosis in the eribulin arm was 72.1 months (95 % CI 13.3-168.3) compared with 43.3 months (95 % CI 9.1-202.0) in the noneribulin arm [hazard ratio (HR): 0.67, 95 % CI 0.47-0.96; P = 0.025]. No significant differences were noted in OS between eribulin used as a first-/second-line or third-/>third-line treatment for MBC. No patient discontinued eribulin therapy due to AEs. In the eribulin arm, grade 4 neutropenia and grade 3 febrile neutropenia were observed in 8 (12.1 %) and 4 (6.1 %) patients, respectively. Eribulin therapy has a survival benefit in Japanese women with ER+/HER2- MBC in routine clinical practice, with no unexpected grade 3/4 AEs. Interestingly, eribulin might be beneficial as any line therapy for ER+/HER2- MBC.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".