A Systematic Review and Meta-analysis of the Combination of Vinorelbine and Lapatinib in Patients With Her2-positive Metastatic Breast Cancer
Bibliographic record
Abstract
The development of effective human epidermal growth factor receptor 2 (HER2)-targeted therapies has been heralded as a significant milestone in breast cancer treatment, resulting in improvement of the outcome for those with HER2-positive metastatic breast cancer. Despite these advantages, metastatic breast cancer is still regarded as an incurable disease. In heavily pretreated patients with increasingly limited options for palliative management, ensuring control of disease and maintenance of quality of life is an important goal. Vinorelbine and lapatinib is a combination used in later-line treatment of metastatic HER2-positive breast cancer. The current article presents a systematic review and meta-analysis of prospective series of the vinorelbine/lapatinib doublet for efficacy and toxicity in metastatic HER2-positive breast cancer. Altogether seven prospective trials involving 235 evaluable patients were retrieved for analysis. Pooled estimates of response rate and disease control rate were 24.4% and 63.3% respectively. Furthermore, overall survival was 20.1 months and progression-free survival was 5.44 months. The most common grade 3 and 4 toxicities were seen in fewer than 10% of cases. Vinorelbine/ lapatinib combination regimen may serve as an option for pre-treated patients with metastatic HER2-positive 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".