75P A multicenter physician survey evaluating Ki-67 use in breast cancer management in Canada
Bibliographic record
Abstract
Ki-67 response to pre-operative endocrine therapy (ET) in early breast cancer is both prognostic and an evidence-based tool to guide adjuvant treatment decisions. While this approach is utilized in many countries, current usage in Canada is unclear. Physicians across Canada were surveyed to explore current practice patterns, perceptions, and perceived barriers to use of Ki-67 in management of early-stage breast cancer. Physicians were invited by email to participate in an anonymous survey using Microsoft Forms and were eligible if they prescribe systemic therapy for breast cancer in Canada. Respondents were asked to describe their usage of Ki-67, perceptions of the evidence surrounding Ki-67 ET response, and interest in future trials using this approach. Responses are summarized descriptively. The survey received 48/163 responses (29.4%), 6 of whom were ineligible. Majority of respondents (97.6%) reported having access to Ki-67 testing upon request. Guiding decisions on adjuvant Abemaciclib was the most common use (97.6%), followed by adjuvant chemotherapy decisions (16.7%), and prognostication (9.52%). Only 19.0% had used Ki-67 response to pre-operative ET in practice, however 69.0% reported they would use it if more easily available. Common barriers identified to this approach included lack of awareness by other providers (54.8%), increased resource requirement (54.8%), lack of timely Medical Oncology consultation prior to surgery (52.4%), potential surgical delays (38.1%), and modest or unclear benefit (19.0%). The majority of physicians (85.3%) reported that they would participate in future trials using Ki-67 endocrine response, and that the number of patients where treatment decisions were changed (95.2%) and cost analysis (42.3%) were important endpoints to include. Despite widespread availability of Ki-67 testing, few physicians in Canada currently use it to assess endocrine response, predominantly due to logistical and resource constraints. There is a high level of interest in participating in future trials using this strategy, which should focus on both disease related outcomes, feasibility, and the financial impact on the public healthcare system.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".