Sentinel lymph node in vulvar cancer
Bibliographic record
Abstract
BACKGROUND: The aim of the study was to assess the feasibility, efficacy, and accuracy of the sentinel lymph node (SLN) procedure in vulvar cancer. METHODS: From April 2004 to September 2006, all patients with vulvar cancer, clinical stages I and II, underwent SLN detection, followed by a complete inguinofemoral lymphadenectomy. Demographic, surgical, and pathologic data on all patients were prospectively entered in a database. RESULTS: Forty-two patients underwent the SLN procedure. One patient was excluded from further analysis due to metastases to the vulva. The detection rate for at least 1 SLN per patient was 95%, with bilateral SLNs detected in 46% of patients. There was a trend toward improved ability to detect bilateral SLNs and proximity of the cancer to the midline (r = 0.996; P = .057). No contralateral SLNs were identified in patients with lateral vulvar lesions (>1 cm from the midline). For 'close-to-midline' (< or =1 cm from the midline) lesions, SLNs were detected in 93% of ipsilateral groins and bilateral SLNs were found in 46% of patients, whereas lesions abutting the midline had unilateral and bilateral SLN detected in 100% and 93%, respectively. Sixteen of 41 patients (39%) and 18 of 68 groins (26%) revealed metastatic disease in the lymph nodes; all were correctly identified by the SLN procedure. There were no false-negative SLN results. CONCLUSIONS: SLN dissection is feasible and safe to perform in vulvar cancer. The ability to identify bilateral sentinel inguinal lymph nodes appears to be related to the proximity of the vulvar cancer to the midline.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".