Defining the Optimal Surgeon Experience for Breast Cancer Sentinel Lymph Node Biopsy: A Model for Implementation of New Surgical Techniques
Bibliographic record
Abstract
OBJECTIVE: To determine the optimal experience required to minimize the false-negative rate of sentinel lymph node (SLN) biopsy for breast cancer. SUMMARY BACKGROUND DATA: Before abandoning routine axillary dissection in favor of SLN biopsy for breast cancer, each surgeon and institution must document acceptable SLN identification and false-negative rates. Although some studies have examined the impact of individual surgeon experience on the SLN identification rate, minimal data exist to determine the optimal experience required to minimize the more crucial false-negative rate. METHODS: Analysis was performed of a large prospective multiinstitutional study involving 226 surgeons. SLN biopsy was performed using blue dye, radioactive colloid, or both. SLN biopsy was performed with completion axillary LN dissection in all patients. The impact of surgeon experience on the SLN identification and false-negative rates was examined. Logistic regression analysis was performed to evaluate independent factors in addition to surgeon experience associated with these outcomes. RESULTS: A total of 2,148 patients were enrolled in the study. Improvement in the SLN identification and false-negative rates was found after 20 cases had been performed. Multivariate analysis revealed that patient age, nonpalpable tumors, and injection of blue dye alone for SLN biopsy were independently associated with decreased SLN identification rates, whereas upper outer quadrant tumor location was the only factor associated with an increased false-negative rate. CONCLUSIONS: Surgeons should perform at least 20 SLN cases with acceptable results before abandoning routine axillary dissection. This study provides a model for surgeon training and experience that may be applicable to the implementation of other new surgical technologies.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".