2022-RA-1165-ESGO Feasibility and safety of inguinofemoral sentinel lymph node biopsy for previously excised vulvar cancer
Bibliographic record
Abstract
Introduction/Background Performing inguinofemoral sentinel lymph node biopsy (IFSLNB) for vulvar cancer following a previous vulvar excision, often referred to as ‘scar injection,’ is currently debated. Our study aimed to assess the feasibility and safety of IFSLNB following scar injection. Methodology We conducted a retrospective observational study of patients with vulvar cancer, who underwent IFSLNB following radiotracer injection around a tumour or around a scar following previous vulvar excision. IFSLN detection rates are described per patient and per groin and are compared using chi-square analysis. We performed a Cox regression analysis to assess the association of recurrence and survival with vulvar injection site and recognized pathological variables. Results Data was analyzed for 173 groins in 97 patients. At least one IFSLN was detected in 94% of groins examined, and IFSLN detection rate did not differ whether the groin was assessed following tumour injection (n=122, 94%) or scar injection (n=40, 93%; p=0.85). Patients in the scar injection group had less frequent IFLN metastases (p=0.019), smaller tumours (p<0.001) and more superficial invasion (p<0.02). Median overall follow-up from surgery to death or censoring was 34.7 (range 0–108) months. Cox regression analysis demonstrated that scar injection was not an independent predictor of recurrence or death, and depth of invasion was the only independent predictor of disease recurrence (HR 1.14, p=0.029). Conclusion Our observations support the feasibility and safety of scar injection as an alternative to full lymphadenectomy and should be validated in a prospective study with a more robust sample size.
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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 | 0.001 |
| 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) | 0.013 | 0.003 |
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".