National Variations in the Work-Up, Investigation, and Surgical Management of Ductal Carcinoma In Situ of the Breast across Canadian Surgeons
Bibliographic record
Abstract
Variation in the management of Ductal Carcinoma In Situ (DCIS) of the breast occur at both national and international levels. The aim of this study is to determine the degree of, and reasons behind, this variation in the workup and treatment of DCIS among Canadian surgeons. We developed a 35-question survey involving the pre-, peri, and post-operative management of DCIS using SurveyMonkey®. The survey was sent out via email and responses were analyzed using SurveyMonkey® and Microsoft Excel. 51/119 (43%) of the Canadian General Surgeons contacted participated in this study. Some variation was observed in the utilization of pre-operative imaging with 29/48 (60%) surgeons routinely using ultrasound. Perceived contraindications to breast conserving therapy also varied with multicentricity (54%) and the presence of diffuse microcalcifications (13%). Nearly all respondent’s (98%) patients had access to immediate breast reconstruction following a mastectomy but 14/48 (29%) of respondents’ patients were required to travel a mean distance of 300 km to undergo the procedure. Substantial variation was also seen during follow-up with half (52%) of surgeons following up patients for >1 month in their surgical clinic. There is considerable variation in the management of DCIS among Canadian Surgeons. The present study indicates the need for pan-Canadian, evidence-based guidelines to ensure a standardized management strategy for patients with DCIS.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".