Sentinel Lymph Node Biopsy: A Rational Approach for Staging T2N0 Oral Cancer
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: For oral cancer patients, the presence of neck nodal metastases is the most important disease prognosticator. However, a significant proportion of clinically N0 patients harbor occult microscopic nodal metastasis. Our objective was to determine the feasibility and accuracy of sentinel node biopsy (SNB) in the staging of T2N0 oral carcinoma patients. STUDY DESIGN: Prospective analysis. METHODS: Twenty patients with previously untreated N0 oral cavity squamous cell carcinoma were studied. Each patient had an SNB performed using preoperative technetium sulfur colloid lymphoscintigraphy, intraoperative gamma probe guidance, and intraoperative peritumoral injection of 1% isosulfan blue. All patients underwent neck dissection. The sentinel lymph nodes (SLNs) were sectioned in 2- to 3-mm intervals, formalin fixed, and sectioned at three levels. The non-SLNs were sectioned in a routine manner for histologic examination. RESULTS: SLNs were identified in all patients (100%) and accurately predicted the pathologic nodal status in 18 of 20 patients (90%). Tumor was found exclusively in the SLNs in six patients (30%). Two patients had positive SLNs at multiple neck levels. Two patients had a negative SLN and a positive non-SLN (false-negative findings). Occult nodal metastases were present in 60% of the cohort. CONCLUSIONS: SNB is a technically feasible and accurate procedure for staging the neck in oral carcinoma patients. However, SNB accuracy is lower for floor of the mouth lesions. The rate of occult nodal metastases identified in this cohort is higher than previously reported in the literature. These results suggest that SNB warrants further multi-institutional studies.
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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 | 0.002 |
| 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) | 0.002 | 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".