Laparoscopic Intragastric Resection
Bibliographic record
Abstract
OBJECTIVE: To present the technique for and early results of laparoscopic intragastric resection (LIGR). BACKGROUND: Treatment of confirmed or suspected submucosal gastric malignancies relies on clear margin resection, for which minimally invasive surgery is widely accepted. However, resection in some localization remains challenging. METHODS: We present the steps of LIGR for gastric submucosal tumors (GSMTs). We report the results of LIGR in consecutive patients operated at 2 institutions, including intraoperative, pathologic, 30-day major morbidity and mortality characteristics. RESULTS: After laparoscopic access to the abdominal cavity, cuffed gastric ports are placed to approximate the anterior gastric wall to the abdominal wall. A pneumogastrum is created. The tumor is resected in the submucosal plane and the deficit closed with intragastric suturing. Specimen extraction is performed perorally or through a gastrotomy site. In 8 proximal intraluminal GSMTs with median size of 3.1 cm (range: 1.8-6.0 cm), median operative time was 167.5 minutes (range: 120-300 mins). There was no major morbidity and no mortality. All resections were R0. CONCLUSIONS: We illustrate the technique of a novel, feasible, and safe minimally invasive approach to GSMTs. LIGR is an alternative to resect challenging GSMTs by limiting surgical invasiveness and preserving gastrointestinal function.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".