National Adoption of Sentinel Node Biopsy for Breast Cancer: Lessons Learned from the Canadian Experience
Bibliographic record
Abstract
Sentinel lymph node biopsy (SLNB) in breast cancer has not been readily adopted into Canadian surgical practice in comparison with the United States. We sought to evaluate current national practice patterns and explore barriers to direct efforts to improve the adoption of SLNB in Canada. All active (n = 1413) general surgeons in Canada were surveyed by mail. Surgeon demographics, practice patterns, skill acquisition and attitudes towards SLNB were assessed. The response rate was 63% (n = 889). Of the 506 (57%) surgeons who treated breast cancer, half were community based with breast surgery comprising <25% of their practices. Most (70%) performed <or=5 breast surgeries/month. Almost all (96%) believed SLNB was standard of care or an acceptable alternative to axillary lymph node dissection (ALND). Of these, 306 (61%) performed SLNB. Predictors of performing SLNB were breast/oncology fellowship (p = 0.03) or greater percentage of practice dedicated to breast (p = 0.02) but not region, type of practice (community versus academic), gender or year of residency completion. Reasons for performing SLNB were decreased morbidity (85%) and enhanced staging (59%) as opposed to competitive pressure (13%). The majority (75%) performed SLNB as a stand-alone procedure for T1/T2 cancers and high-risk ductal carcinoma in situ (70%). Almost half (46%) abandoned back up ALND after 30 or fewer cases even though the majority (75%) acknowledged the false-negative rate should be <5%. Most (76%) learned SLNB through mentoring or a formal course/residency. Of the 197 (39%) not performing SLNB, 53% felt that inadequate access to nuclear medicine and gamma probe equipment was the predominant barrier. SLNB has been adopted into Canadian surgical practice. The majority of surgeons believe that SLNB is an acceptable alternative to ALND, with 61% now performing SLNB compared to 27% in 2001. Barriers to implementation appear to be related to inadequate resources as opposed to lack of belief in the procedure.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".