A systematic review and pooled analysis of retrospective series of eribulin in metastatic breast cancer
Bibliographic record
Abstract
Eribulin is one of the newer chemotherapeutic agents approved for use in later line treatment of patients with metastatic breast cancer. Phase III studies have shown a useful clinical response rate for eribulin as well as equivalence to another commonly used drug in metastatic breast cancer, capecitabine. Nevertheless, whether this clinical value observed in trial patients is maintained in patients seen in clinical oncology practice, outside trials remains a question. Several series published over the last few years sought to answer this question. The current paper carries out a pooled analysis of these retrospective series to obtain efficacy and toxicity data for eribulin in metastatic breast cancer patients treated off trials. Thirteen series with a total of 1095 patients were identified. Pooled estimates of response rate and clinical benefit rate were 20.1% (95% confidence interval: 16.3-23.9%) and 46.3% (95% confidence interval: 39.4-53.2%) respectively. These were somewhat higher than the response rate and clinical benefit rate observed in a pooled analysis of two randomized phase III trials (14.9 and 30.9%, respectively, conducted in an intension-to-treat manner). In contrast overall survival was longer in the phase III trials (median 15.2 months) than in the retrospective studies (pooled estimate 9.8 months). All grades toxicities were similar in practice compared with trials with slightly higher grade 3 toxicities (46.1 vs. 38.7%) but lower grade 4 toxicities (17.2 vs. 27.7%) in patients off trials.
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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 | 0.021 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".