Outcome of eribulin as a late treatment line for Thai metastatic breast cancer patients
Bibliographic record
Abstract
Background: We report the safety and efficacy of eribulin as a late treatment line in Thai metastatic breast cancer (MBC) patients. Patients and methods: A total of 30 MBC patients treated with eribulin between January 2014 and January 2017 were retrospectively analyzed. The patients were scheduled to receive 1.4 mg/m2 of eribulin on day 1, day 8 and subsequently every 21 days. All patients had previously received at least three chemotherapy regimens including anthracycline and taxane. Response rate and progression-free survival (PFS) were analyzed. Results: The median age was 56 years (range, 40–74 years), with a median follow-up time of 5.7 months (range, 0.2–25 months). The overall response rate was 30% (nine patients): four patients had triple-negative breast cancer, three patients had luminal B breast cancer and two patients had luminal A breast cancer. The median PFS was 2.9 months (range, 0.2–14 months). The median number of previous chemotherapy regimens was 4 (range, 3–9). Univariate analysis showed that the number of regimens (four or fewer) prior to eribulin was statistically associated with superior PFS ( P = 0.009). Multivariate analysis also showed similar statistical association between number of prior regimens (four or fewer) and better PFS adjusted by age group (≥50 years; hazard ratio = 1.29; 95% CI: 1.0–1.65; P = 0.046). There were no toxic deaths or grade 4 toxicities. Nine (30%) patients had grade 3 anemia toxicities, and the other common toxicities were leukopenia and neutropenia. Four (13%) patients required dose reduction and 16 (53%) patients required dose delay because of toxicities. Conclusion: Eribulin is an effective drug for heavily pretreated MBC patients with tolerable toxicities. The benefit was superior in patients who received fewer than four previous chemotherapy regimens. Keywords: eribulin, metastatic breast cancer, late treatment line
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".