False-negative sentinel lymph node biopsy for melanoma: a single-surgeon experience
Bibliographic record
Abstract
BACKGROUND: The status of the regional lymph node basin is of prognostic importance in patients with melanoma, making the performance of sentinel lymph node biopsies (SLNBs) a key component of patient care management, particularly with the advent of immunotherapy for adjuvant treatment. The primary goal of our study was to assess the false-negative rate of SLNBs among patients with melanoma. METHODS: We conducted a retrospective review of patients with melanoma undergoing SLNB by a single surgeon between Jan. 1, 2005, and Dec. 31, 2020. We extracted and cross-referenced patient demographic and pathologic information. RESULTS: During the study period, 501 patients underwent an SLNB. Of these, 97 (19.4%) patients had pathologically positive sentinel lymph nodes and 404 (80.6%) patients had negative results. The latter were subject to further review; 84 (20.8%) patients subsequently developed recurrence, with 25 (6.2%) recurrences within the primary nodal basin. Isolated regional recurrence occurred in 11 (2.7%) patients and conjunction with a false-negative rate was 10.2%. Unadjusted recurrence rates were similar across each lymph node basin, including the axilla (2.7%), groin (3.6%), and neck (1.4%). CONCLUSION: The false-negative SLNB rate was 10.2% for isolated regional recurrences. These findings need to be considered in the era of using adjuvant systemic therapy for patients with melanoma.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".