Going Beyond Anthracyclines and Taxanes in Breast Cancer What’s Next?
Bibliographic record
Abstract
In recent years, a clear trend has been observed for taxanes to be used earlier in the course of breast cancer, with a large proportion of patients previously treated with anthracyclines and/or taxanes in the (neo)adjuvant setting. In addition, tolerability issues associated with taxane use in elderly patients and patients with substantial comorbidity, means that taxane use is frequently compromised in a substantial proportion of patients with metastatic breast cancer (MBC). Retreatment with taxanes yields variable results, and alternative cytotoxic agents with good activity in patients with anthracycline- and taxane-pretreated MBC are required. Large studies and meta-analyses have helped to establish the role of anthracycline- and taxane-based adjuvant therapy for early breast cancer (EBC). Addition of further cytotoxic agents has generally met with little success, thus the focus has moved towards optimization of adjuvant therapy through scheduling and patient selection. This review considers recent and ongoing developments in the chemotherapeutic management of EBC and MBC. Keywords: Anthracyclines, capecitabine, chemotherapy, early breast cancer, metastatic breast cancer, taxanes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".