Vinorelbine with or without Trastuzumab in Metastatic Breast Cancer: A Retrospective Single Institution Series
Bibliographic record
Abstract
Background. We report our experience with vinorelbine, a widely used chemotherapeutic, in unselected metastatic breast cancer patients treated in clinical routine. Patients and Methods. The data of all patients with metastatic breast cancer receiving vinorelbine with or without trastuzumab during a six year period were reviewed. Patients received vinorelbine intravenous 25-30 mg/m(2) or 60-80 mg/m(2) orally in days 1 and 8 of a 21 day cycle. Results. Eighty-seven women were included. Sixty-two patients received vinorelbine alone and 25 patients received vinorelbine in combination with trastuzumab. In 67 patients this was the first line treatment for metastatic disease and in 20 patients it was 2nd or later line of treatment. The median TTP was six months (range: 1-45). The median overall survival was 11.5 months (range: 1-83). Seventy patients were evaluable for response. In patients receiving first line treatment 44.4% had a response while in the second and subsequent lines setting 12.5% of patients responded (P = 0.001). Objective response was obtained in 63.6% of patients receiving concomitant trastuzumab and in 25% of patients receiving vinorelbine alone (P = 0.0002). Conclusion. This study confirms a high disease control rate. Response rate and TTP were superior in first line treatment compared to subsequent lines.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".