Guidance for Canadian Breast Cancer Practice: National Consensus Recommendations for Clinical Staging of Patients Newly Diagnosed with Breast Cancer
Bibliographic record
Abstract
The accurate staging of breast cancer is fundamental for guiding treatment decisions and predicting patient outcomes. However, there can be considerable variation in routine clinical practice based on individual interpretation of guidelines and depending on the healthcare provider initially involved in working up patients newly diagnosed with breast cancer, ranging from primary care providers, triage nurses, surgeons, and/or oncologists. The optimal approach for clinical staging, particularly in asymptomatic patients presenting with intermediate-risk disease, remains a topic of dialogue among clinicians. Given this area of uncertainty, the Research Excellence, Active Leadership (REAL) Canadian Breast Cancer Alliance conducted a modified Delphi process to assess the level of agreement among Canadian expert clinicians on various staging recommendations. In total, 20 items were drafted covering staging based on biological status, the utilization of localization clips, both for the axilla during diagnosis and primary surgical site for margins and radiation therapy planning, and the use of advanced imaging for the investigation of distant metastases. Overall, the consensus threshold among all participants (i.e., ≥75% agreement) was reached in 20/20 items. Differences in clinical practice and recent findings from the literature are provided in the discussion. These consensus recommendations are meant to help standardize breast cancer staging practices in Canada, ensuring accurate diagnosis and optimal treatment planning.
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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 | 0.059 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".