Adequacy of invasive and in situ breast carcinoma margins in radioactive seed and wire‐guided localization lumpectomies
Bibliographic record
Abstract
Image-guided preoperative localizations help surgeons to completely resect nonpalpable breast cancers. The objective of this study is to compare the adequacy of specimen margins for both invasive breast cancer (IBC) and ductal carcinoma in situ (DCIS) after radioactive seed localization (RSL) vs wire-guided localization (WGL). We retrospectively reviewed 600 cases at a single Canadian academic center from January 2014 to September 2017, comparing surgical margins, re-excisions and reoperations, localization accuracy and major complications (migration, accidental deployment, vasovagal reaction), as well as operative duration between RSL and WGL cases. IBC margins were positive in 7% of RSL and 6% of WGL cases (P = .57). Tumor size (P = .039) and association with DCIS (P = .036) predicted positive margins in invasive carcinoma. DCIS margins were positive in 6% and 8%, and close (≤2 mm) in 37% and 36% of cases (P = .45) for RSL and RSL cases respectively. The presence of extensive intraductal component predicted positive DCIS margins (P < .0001). There was no significant difference between intraoperative re-excisions (P = .54), localization accuracy (P = .34), and operation duration (P = .81). Reoperation for lumpectomies and mastectomies was marginally higher for WGL than RSL (P = .049). There were 11 (4%) WGL and no RSL complications (P = .03). Overall, positive margins for IBC, close or positive margins for DCIS, intraoperative re-excision, localization accuracy, and operation duration were similar between RSL and WGL. The reoperation rate was higher in WGL than RSL, which may reflect practice changes over time. RSL was safer than WGL with lower complication rates.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".