Taxanes Alone or in Combination With Anthracyclines As First-Line Therapy of Patients With Metastatic Breast Cancer
Bibliographic record
Abstract
PURPOSE: Taxanes (paclitaxel or docetaxel) have been sequenced or combined with anthracyclines (doxorubicin or epirubicin) for the first-line treatment of advanced breast cancer. This meta-analysis uses data from all relevant trials to detect any advantages of taxanes in terms of tumor response, progression-free survival (PFS), and survival. PATIENTS AND METHODS: Individual patient data were collected on eight randomized combination trials comparing anthracyclines + taxanes (+ cyclophosphamide in one trial) with anthracyclines + cyclophosphamide (+ fluorouracil in four trials), and on three single-agent trials comparing taxanes with anthracyclines. Combination trials included 3,034 patients; single-agent trials included 919 patients. RESULTS: Median follow-up of living patients was 43 months, median survival was 19.3 months, and median PFS was 7.1 months. In single-agent trials, response rates were similar in the taxanes (38%) and in the anthracyclines (33%) arms (P = .08). The hazard ratios for taxanes compared with anthracyclines were 1.19 (95% CI, 1.04 to 1.36; P = .011) for PFS and 1.01 (95% CI, 0.88 to 1.16; P = .90) for survival. In combination trials, response rates were 57% (10% complete) in taxane-based combinations and 46% (6% complete) in control arms (P < .001). The hazard ratios for taxane-based combinations compared with control arms were 0.92 (95% CI, 0.85 to 0.99; P = .031) for PFS and 0.95 (95% CI, 0.88 to 1.03; P = .24) for survival. CONCLUSION: Taxanes were significantly worse than single-agent anthracyclines in terms of PFS, but not in terms of response rates or survival. Taxane-based combinations were significantly better than anthracycline-based combinations in terms of response rates and PFS, but not in terms of survival.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".