Preoperative triage and multidisciplinary consultation for patients with breast cancer: A pilot study between surgery and medical oncology.
Bibliographic record
Abstract
208 Background: Breast cancer management has become exceedingly collaborative across specialties. Preoperative multidisciplinary input in particular is increasingly needed as indications for neoadjuvant therapy (NAT) continue to expand. In British Columbia, as in many other regions, breast cancer surgery is typically performed by community surgeons working in settings away from medical and radiation oncologists, making preoperative multidisciplinary input challenging. Methods: This prospective pilot study was designed to enhance and streamline preoperative breast cancer care at the BC Cancer Agency Vancouver Centre (BCCA) and Mount Saint Joseph’s Hospital (MSJ). Patients with a new diagnosis of breast cancer at the MSJ Breast Clinic are included in this pilot triage project if tumors meet one or more of the following criteria: triple negative, HER2+, clinically palpable ≥ 2cm, or present with positive/palpable nodes. Before the patient has consulted with a surgeon, the pathology, imaging, and GP’s assessment is faxed by a MSJ nurse navigator to BCCA for preoperative triage by a medical oncologist. A BCCA navigator facilitates this confidential electronic triage process, and communicates the medical oncologist’s recommendation [a) urgent referral for NAT, or b) upfront surgery] back to MSJ for appropriate booking and management. Results: Since inception in November 2014, 42 patients have undergone electronic preoperative triage through this pilot project. Of those, 47.6% were recommended to have, and did have, a preoperative medical oncology consult to discuss NAT. 90% of patients who had a NAT consult ultimately received NAT compared to a 66% uptake of NAT through the traditional referral process (P = 0.029). Median wait time from ‘biopsy result’ to ‘start of chemotherapy’ was significantly reduced by 9 days through this novel triage process (P = 0.047). Conclusions: This preoperative multidisciplinary triage project has significantly reduced treatment wait times and improved patient selection for and uptake of NAT for breast cancer. Breast cancer care benefits from increased preoperative navigation to help streamline and expedite care for high-risk patients.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".