Safety and effectiveness of eribulin in Japanese patients with locally advanced or metastatic breast cancer: a post-marketing observational study
Bibliographic record
Abstract
Summary Background This large-scale study was conducted to evaluate the safety and effectiveness of eribulin for the treatment of inoperable or recurrent breast cancer in real-world settings in Japan. Methods Between July and December 2011, eligible patients with inoperable or recurrent breast cancer receiving eribulin for the first time were centrally registered and observed for 1 year. Eribulin was administered intravenously (1.4 mg/m2) on days 1 and 8 of every 3-week cycle. The primary endpoint was the frequency and intensity of adverse drug reactions (ADRs). Secondary endpoints included overall response rate (ORR) and time to treatment failure (TTF). Results Of 968 patients registered at 325 institutions, 951 and 671 were included in the safety and effectiveness analyses, respectively. In the safety population, ADRs were observed in 841 patients (88.4%). The most common (≥15% incidence) were neutropenia (66.6%), leukopenia (62.4%), lymphopenia (18.4%), and peripheral neuropathy (16.8%). The most common grade ≥ 3 ADRs (>5% incidence) were neutropenia (59.8%), leukopenia (50.5%), lymphopenia (16.1%), and febrile neutropenia (7.7%). In the effectiveness population, ORR was 16.5% (95% confidence interval: 13.7, 19.4). The median TTF was 127 days (95% confidence interval: 120, 134). Conclusions The safety and effectiveness profile of eribulin was consistent with prior studies. Eribulin had a favorable risk-benefit balance when used in real-world clinical settings.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".