Do Anthracyclines Still Have a Role in Adjuvant Chemotherapy of Breast Cancer?
Bibliographic record
Abstract
Anthracycline-based regimens became the standard of care for early breast cancer patients based on the survival advantage they provide over nonanthracycline-containing regimens. The addition of taxanes, and subsequently trastuzumab in HER2-overexpressing patients, to anthracyclines further improved their efficacy in several studies involving high-risk early breast cancer patients. Concern over toxicity initially surfaced after anthracyclines were reported to carry an increased risk of cardiotoxicity and secondary leukemia. Trastuzumab has since been shown to compound the risk of cardiotoxicity in patients who have received an anthracycline. This has led to the development of regimens featuring a taxane without an anthracycline; these protocols vary in design and have different toxicity and efficacy profiles. Ongoing investigations are centered on the optimization of nonanthracycline regimens, prospective exploration of molecular markers to identify populations of patients who will derive maximal benefit from anthracycline-based chemotherapy, and the identification of less cardiotoxic formulations of existing anthracycline agents. Perhaps most importantly, a rapidly growing understanding of the biological heterogeneity of breast cancer is likely to lead to an individualized standard of care guided by particular patient and tumor characteristics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".