Canadian Recommendations on Optimal Breast Biopsy Practices Developed Using a Modified Delphi Panel
Bibliographic record
Abstract
There are few recommendations in Canada to assist clinicians in selecting appropriate biopsy techniques (fine-needle aspiration, core-needle biopsy, vacuum-assisted biopsy, vacuum-assisted excision) and imaging technologies (mammography, ultrasound, magnetic resonance imaging, contrast-enhanced mammography) for biopsy guidance. Limited existing recommendations from other countries do not consider the unique aspects of the Canadian healthcare system. To address this gap, 17 experts participated in a modified Delphi panel to reach consensus on biopsy-related topics and provide recommendations. The panel was comprised of 12 radiologists, 2 pathologists, and 3 surgeons from 6 provinces across Canada. Panelists engaged in two rounds of anonymized voting, with an in-person discussion held between the rounds. The modified Delphi panel adhered to best practices, including establishing consensus definitions prior to voting, utilizing anonymized voting, and abstaining from communication among panelists before the in-person meeting. A rigorous statistical approach was utilized to analyze the points of agreement and disagreement. Consensus findings covered a wide range of topics, including recommendations for initial biopsy technique based on lesion type and imaging modality, patient management or rebiopsy considerations after the initial biopsy, procedural recommendations (i.e., gauge size, number of samples), patient considerations (i.e., drug allergies, pregnancy). Overall, 347 individual items were included in the final analysis, 286 (82%) of which achieved consensus. These consensus recommendations intend to offer general recommendations to help standardize and improve practices across Canada and were endorsed by the Canadian Society of Breast Imaging. However, they should be evaluated in the context of each individual case and emerging evidence.
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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 | 0.117 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".