The Canadian Breast Cancer Symposium 2023 Meeting Report
Bibliographic record
Abstract
On 15–16 June 2023, healthcare professionals and breast cancer patients and advocates from across Canada met in Toronto, Ontario, for the 2023 Canadian Breast Cancer Symposium (CBSC.). The CBSC. is a national, multidisciplinary event that occurs every 2 years with the goal of developing a personalized approach to the management of breast cancer in Canada. Experts provided state-of-the-art information to help optimally manage breast cancer patients, including etiology, prevention, diagnosis, experimental biology, and therapy of breast cancer and premalignant breast disease. The symposium also had the objectives of increasing communication and collaboration among breast cancer healthcare providers nationwide and providing a comprehensive and real-life review of the many facets of breast cancer. The sessions covered the patient voice, the top breast cancer papers from different disciplines in 2022, artificial intelligence in breast cancer, systemic therapy updates, the management of central nervous system metastases, multidisciplinary management of ductal carcinoma in situ, special populations, optimization-based individual prognostic factors, toxicity management of novel therapeutics, survivorship, and updates in surgical oncology. The key takeaways of these sessions have been summarized in this conference report.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".