Role of Patient and Disease Factors in Adjuvant Systemic Therapy Decision Making for Early-Stage, Operable Breast Cancer: American Society of Clinical Oncology Endorsement of Cancer Care Ontario Guideline Recommendations
Bibliographic record
Abstract
PURPOSE: An American Society of Clinical Oncology (ASCO) panel considered the Cancer Care Ontario (CCO) recommendations on the role of patient and disease factors in selecting adjuvant therapy for women with early-stage breast cancer for endorsement. METHODS: ASCO staff reviewed the CCO guideline for methodologic rigor, and an ASCO panel of content experts reviewed the content of the recommendations. CCO RECOMMENDATIONS: For making decisions regarding adjuvant therapy, nodal status, tumor size, estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2) status, tumor grade, and lymphovascular invasion are relevant; Oncotype DX score and Adjuvant! Online may be used as risk stratification tools; and age, menopausal status, and medical comorbidities should be considered. Chemotherapy should be considered for patients with positive lymph nodes, ER-negative disease, HER2-positive disease, Adjuvant! Online mortality greater than 10%, grade 3 lymph node-negative tumors (T > 5 mm), triple-negative (ER-negative, PgR-negative, HER2-negative) tumors, lymphovascular invasion positivity, or estimated distant relapse risk of greater than 15% at 10 years based on Oncotype DX recurrence score (RS). Chemotherapy may not be beneficial or required for small node-negative tumors (T < 5 mm) without high-risk features or for patients with HER2-negative, strongly ER-positive, and PgR-positive cancer with micrometastatic nodal disease, T less than 5 mm, or Oncotype DX RS with an estimated distant relapse risk of less than 15% at 10 years. ASCO PANEL CONCLUSION: The ASCO panel endorses the recommendations with minor suggested revisions and highlights three areas that warrant further consideration: tumor histology and adjuvant therapy recommendations, risk stratification tools and proposed Oncotype DX RS thresholds to guide decisions about chemotherapy, and patient factors in decision making.
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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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".