Guidance for Canadian Breast Cancer Practice: National Consensus Recommendations for the Systemic Treatment of Patients with HER2+ Breast Cancer in Both the Early and Metastatic Setting
Bibliographic record
Abstract
Human epidermal growth factor receptor 2-positive (HER2+) breast cancer is an aggressive subtype of breast cancer associated with a poor prognosis when sub-optimally treated. Recent advances include new and effective targeted therapies that have significantly improved outcomes for patients. Despite these advances, there are significant gaps across Canada, underscoring the need for evidence-based consensus guidance to inform treatment decisions. Addressing these gaps is crucial to ensuring that effective therapies are integrated into clinical practice, so as to improve the lives of patients affected by this aggressive form of breast cancer. The Research Excellence, Active Leadership (REAL) Canadian Breast Cancer Alliance is a standing nucleus committee of clinical-academic oncologists across Canada and Breast Cancer Canada, a patient organization. The mandate of this group is to provide evidence-based guidance on best practices in the management of patients with breast cancer. These consensus recommendations were developed using a modified Delphi process with up to three rounds of anonymous voting. Consensus was defined a priori as ≥75% of voters agreeing with the recommendation as written. There are 9 recommendations in the early setting; 7 recommendations in the metastatic setting; and 10 recommendations for patients with brain metastases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.074 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".