Endoscopic submucosal dissection of gastric neoplastic lesions in Western countries: systematic review and meta-analysis
Bibliographic record
Abstract
Endoscopic submucosal dissection (ESD) for gastric neoplastic lesions removal is largely performed in Asian countries. Unfortunately, ESD diffusion, particularly for gastric lesion removal, is still limited in Western countries. We performed a systematic review of available data coming from Western centers. The en bloc and the R0 resection rates for all neoplastic lesions, including early gastric cancer (EGC) and dysplasia, were calculated, as well as the curative rate for EGC. Complications and the 1-month mortality rates were computed. A total of 22 studies from Europe (N = 15), Latina America (N = 6), and Canada (N = 1) were retrieved, with 1152 patients and 1210 lesions. The en bloc resection was successful in 96% [95% confidence interval (CI) 93-98] with a significant heterogeneity (I2 = 63.5%; P < 0.0001). The R0 was achieved in 84% (95% CI 79-89; I2 = 79.9%; P < 0.001). The resection rate was curative in 72% out of 340 patients with EGC (95% CI 65-79, I2 = 8%; P = 0.36). Overall, complications occurred in 9.5% of patients, including bleeding (5.8%), perforation (3.4%), and stenosis (0.35%). A total of three (0.26%) patients deceased within 1 month, but none was directly related to the procedure. Lesion recurrence was observed in 38 (3.5%; 95% CI 2.3-4.4) cases, including 21 EGC and 17 dysplasia. In Western countries, the en bloc and the R0 resections were successful in the large majority of cases, whilst the resection was curative in 72% of patients with EGC. The complications rate was acceptably low.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".