Adequate gross resection margin length ensuring pathologically complete resection in gastrectomy for gastric cancer: A systematic review and <scp>meta‐analysis</scp>
Bibliographic record
Abstract
Aim: A positive resection margin (RM) is associated with poor survival after gastrectomy for gastric cancer (GC). However, the adequate RM length to avoid a positive RM remains controversial. We performed a systematic review to examine the RM length required to avoid a positive RM in gastrectomy for GC. Methods: This systematic review involved all relevant articles identified in PubMed, the Cochrane Library, Web of Science, and ClinicalTrials.gov until August 2023. The incidence of a positive RM related to the RM length and the possible incidence of a positive RM estimated from the discrepancy between the gross and pathological RM length were evaluated. The Newcastle-Ottawa Scale was used to quantify study quality. Results: Thirteen studies involving 8983 patients were analyzed. Investigation of the incidence of a positive RM in relation to the RM length showed that a proximal RM length of 6 cm guaranteed a negative RM in gastrectomy. Analyses of the possible incidence of a positive RM revealed that a negative RM would be guaranteed if the proximal RM length was 6 cm in distal gastrectomy, if the esophageal resection length was 2 cm in total gastrectomy for GC without esophageal invasion and 2.5 cm in total or proximal gastrectomy for GC with esophageal invasion or esophagogastric junction cancer, and if the distal RM length was 4 cm in proximal gastrectomy for early GC. Conclusions: The adequate RM lengths to ensure a pathologically negative RM in each type of gastrectomy for GC were herein suggested.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.018 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".