Appraisal of Systemic Treatment Strategies in Early HER2-Positive Breast Cancer—A Literature Review
Bibliographic record
Abstract
BACKGROUND: The overexpression of the human epidermal growth factor receptor 2 (HER2+) accounts for 15-20% of all breast cancer phenotypes. Even after the completion of the standard combination of chemotherapy and trastuzumab, relapse events occur in approximately 15% of cases. The neoadjuvant approach has multiple benefits that include the potential to downgrade staging and convert previously unresectable tumors to operable tumors. In addition, achieving a pathologic complete response (pCR) following preoperative systemic treatment is prognostic of enhanced survival outcomes. Thus, optimal evaluation among the suitable strategies is crucial in deciding which patients should be selected for the neoadjuvant approach. METHODS: A literature search was conducted in the Embase, Medline, and Cochrane electronic libraries. CONCLUSION: The evaluation of tumor and LN staging and, hence, stratifying BC recurrence risk are decisive factors in guiding clinicians to optimize treatment decisions between the neoadjuvant versus adjuvant approaches. For each individual case, it is important to consider the most likely postsurgical outcome, since, if the patient does not obtain pCR following neoadjuvant treatment, they are eligible for adjuvant T-DM1 in the case of residual disease. This review of HER2-positive female BC outlines suitable neoadjuvant and adjuvant systemic treatment strategies for guiding clinical decision making around the selection of an appropriate therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".