Extending Neoadjuvant Care through Multi-Disciplinary Collaboration: Proceedings from the Fourth Annual Meeting of the Canadian Consortium for Locally Advanced Breast Cancer
Bibliographic record
Abstract
The use of systemic therapy before surgery (“neoadjuvant therapy”) is the standard of care for the treatment of locally advanced and nonoperable breast cancer. The advantages of neoadjuvant therapy include improved rates of breast-conserving surgery, the possibility of early measurement of response, and potentially improved outcomes for certain subgroups of high-risk patients. The use of neoadjuvant therapy in operable breast cancer is increasing, although there are no clear guidelines in Canada to help guide patient selection and management. Multidisciplinary experts in the diagnosis and treatment of locally advanced breast cancer (labc) converged at the fourth annual meeting of the Canadian Consortium for LABC (colab) to further their goals of improved standards for neoadjuvant care and clinical research through education and collaboration. Canadian clinical researchers were joined by Dr. Michael Untch of the Helios Hospital Berlin–Buch—representing the German neoadjuvant treatment groups German Gynecologic Oncology Working Group (Arbeitsgemeinschaft Gynakologische Onkologie) and German Breast Group—to discuss the advancement of research in the neoadjuvant setting and important issues of clinical care and investigator-led research. The group reached a consensus on the importance of multidisciplinary collaboration, the use of clips to mark tumour location, and core biopsy testing for the estrogen and progesterone receptors and the human epidermal growth factor receptor 2 at the time of diagnosis. Other initiatives—including creation of a prospective database, inception of the colab Neoadjuvant Network, and development of a clinical survey to evaluate current practice—continue to further the colab mandate of transforming the neoadjuvant treatment landscape in Canada.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".