Sentinel Lymph Node Biopsy Predicts Lymph Node Metastasis in Early Gastric Cancer: A Retrospective Analysis
Bibliographic record
Abstract
BACKGROUND/AIMS: Minimally invasive treatments have emerged as the frontline therapy for patients with early gastric cancer (EGC). However, some cT1N0 patients with EGC may have lymph node metastasis because of inadequate evaluation. This study aimed to investigate the diagnostic accuracy of sentinel lymph node (SLN) and tried to find out feasible criteria for SLN-guided minimally invasive surgery for EGC. METHODS: A solitary metastasis lymph node was taken as SLN, the features of lymph node metastasis were analyzed retrospectively in 255 patients with EGC, and the result was then compared with a SLN biopsy in 23 patients with EGC. RESULTS: Depth of invasion and tumor size were independent risk factors for lymph node metastasis in EGC. The lymph node metastasis rate for mucosal carcinoma with a diameter <4 cm was 2.5%, and it was 13.3% when the diameter was ≥ 4 cm (p = 0.040). For submucosal carcinoma, it was 25.4% when the tumor diameter was <3 cm and 50.5% when the diameter was ≥ 3 cm (p = 0.003). The accuracy, sensitivity, and specificity of SLN biopsy in EGC was 100%, respectively. The distribution characteristics of SLN were consistent with those of lymph node metastasis in EGC. CONCLUSIONS: SLN-guided minimally invasive surgery could be safely performed in EGC according to feasible criteria.
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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 | 0.001 | 0.001 |
| 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".