Docetaxel first-line therapy in HER2-negative advanced breast cancer: a cohort study in patients with prospectively determined HER2 status
Bibliographic record
Abstract
Docetaxel is one of the most active cytotoxic drugs against breast cancer, but data are lacking on specific activity in molecularly selected subgroups. This retrospective study was aimed at assessing the outcome and prognostic factors for survival of patients with HER2-negative tumors receiving first-line docetaxel-based chemotherapy for advanced breast cancer (ABC). The medical charts of all 162 patients with prospectively proven HER2-negative ABC and having received docetaxel as first-line chemotherapy for metastatic disease at our institution were retrospectively reviewed with special emphasis on docetaxel efficacy. Potential prognostic factors were sought using multivariate analysis. Median progression-free survival (PFS) was 12 months (95% confidence interval 9.7-14.8) and median overall survival (OS) was 34.9 months (95% confidence interval 28.1-52.1). Hormone receptor (HR) status was the strongest prognostic factor in the univariate analysis for both PFS [hazard ratio = 0.23; P = 0.00000063] and OS (hazard ratio = 0.35; P = 0.0000079). After multivariate analysis, only three independent variables for PFS (HR-positive tumor, no prior adjuvant/neoadjuvant chemotherapy, and isolated bone metastases) and two for OS (HR-positive tumor and isolated bone metastases) remained predictive of a favorable outcome. HER2-negative, HR-positive ABC patients have a relatively good prognostic after docetaxel-containing first-line therapy. The subset of HER2-negative, HR-negative (triple-negative) has a very poor outcome, and innovative therapies are eagerly awaited for these patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".