Selection of Optimal Adjuvant Chemotherapy and Targeted Therapy for Early Breast Cancer: ASCO Clinical Practice Guideline Focused Update
Bibliographic record
Abstract
Purpose To update key recommendations of the ASCO guideline adaptation of the Cancer Care Ontario guideline on the selection of optimal adjuvant chemotherapy regimens for early breast cancer and adjuvant targeted therapy for breast cancer. Methods An Expert Panel conducted targeted systematic literature reviews guided by a signals approach to identify new, potentially practice-changing data that might translate to revised practice recommendations. Results The Expert Panel reviewed phase III trials that evaluated adjuvant capecitabine after completion of standard preoperative anthracycline- and taxane-based combination chemotherapy by patients with early-stage breast cancer HER2-negative breast cancer with residual invasive disease at surgery; the addition of 1 year of adjuvant pertuzumab to combination chemotherapy and trastuzumab for patients with early-stage, HER2-positive breast cancer; and the use of neratinib as extended adjuvant therapy for patients after combination chemotherapy and trastuzumab-based adjuvant therapy with early-stage, HER2-positive breast cancer. Recommendations Patients with early-stage HER2-negative breast cancer with pathologic, invasive residual disease at surgery following standard anthracycline- and taxane-based preoperative therapy may be offered up to six to eight cycles of adjuvant capecitabine. Clinicians may add 1 year of adjuvant pertuzumab to trastuzumab-based combination chemotherapy in patients with high-risk, early-stage, HER2-positive breast cancer. Clinicians may use extended adjuvant therapy with neratinib to follow trastuzumab in patients with early-stage, HER2-positive breast cancer. Neratinib causes substantial diarrhea, and diarrhea prophylaxis must be used. Additional information can be found at www.asco.org/breast-cancer-guidelines .
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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 | 0.024 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".