Lymph node mapping in gastric cancer: a pilot study in Western patients
Bibliographic record
Abstract
In East Asia, the role of lymph node (LN) mapping in assisting surgical lymphadenectomy, which is integral to the management of gastric cancer, has been explored. We sought to evaluate its safety and utility in Western patients. Thirteen patients with nonmetastatic gastric adenocarcinoma received endoscopic, peritumoural, submucosal indocyanine green fluorescence (ICG) injections before surgery, and ICG was assessed intraoperatively using a laparoscopic detection system. All patients underwent a laparoscopic subtotal gastrectomy, and 10 of them received D2 lymphadenectomies. ICG-mapped LNs fell outside the D1 distribution in all cases, outside the D1+ distribution in 54%, and within the D2 distribution in all cases. There were no ICG-related allergies, procedural complications, or perioperative deaths. We conclude that ICG LN mapping is safe and feasible in assisting LN dissections and localizing the primary tumour in Western patients. D2 dissections should be performed in patients with advanced gastric cancer, as LNs drained outside the D1/D1+ distributions in most cases.
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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 | 0.000 | 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 | 0.001 | 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".