Meta-analysis of sentinel lymph node biopsy at the time of prophylactic mastectomy of the breast
Bibliographic record
Abstract
BACKGROUND: Prophylactic mastectomy is performed to decrease the risk of breast cancer in women at high risk for the disease. The benefit of sentinel lymph node biopsy (SLNB) at the time of prophylactic mastectomy is controversial, and we performed a meta-analysis of the reported data to assess that benefit. METHODS: We searched MEDLINE, EMBASE and the Cochrane Library databases from January 1993 to December 2009 for studies on patients who underwent SLNB at the time of prophylactic mastectomy. Two reviewers independently evaluated all the identified papers, and only retrospective studies were included. We used a mixed-effect model to combine data. RESULTS: We included 6 studies in this review, comprising a total study population of 1251 patients who underwent 1343 prophylactic mastectomies. Of these 1343 pooled prophylactic mastectomies, the rate of occult invasive cancer (21 cases) was 1.7% (95% confidence interval [CI] 1.1%-2.5%), and the rate of positive SLNs (23 cases) was 1.9% (95% CI 1.2%-2.6%). In all, 36 cases (2.8%, 95% CI 2.0%-3.8%) led to a significant change in surgical management as a result of SLNB at the time of prophylactic mastectomy. In 17 cases, patients with negative SLNs were found to have invasive cancer at the time of prophylactic mastectomy and avoided axillary lymph node dissection (ALND). In 19 cases, patients with positive SLNBs were found not to have invasive cancer at the time of prophylactic mastectomy and needed a subsequent ALND. Of the 23 cases with positive SLNs, about half the patients had locally advanced disease in the contralateral breast. CONCLUSION: Sentinel lymph node biopsy is not suitable for all patients undergoing prophylactic mastectomy, but it may be suitable for patients with contralateral, locally advanced breast cancer.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.058 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".