Comparison of endoscopic submucosal dissection with surgical gastrectomy for early gastric cancer: An updated meta-analysis
Bibliographic record
Abstract
BACKGROUND: There are several surgical options for treating early gastric cancers (EGCs), such as endoscopic resection, laparoscopic or open gastrectomy with D1 or D2 lymphadenectomy. Endoscopic resection for EGC with low risk of lymph node metastasis has been widely accepted as a therapeutic alternative. The role of endoscopic submucosal dissection (ESD) in treating EGC is not well established, especially when compared with resection surgery cases in a long-term follow-up scope. AIM: To compare the safety and efficacy of the short- and long-term outcomes between ESD and resection surgery. METHODS: We searched the databases of PubMed, EMBASE, Web of Science, and the Cochrane Library from January 1990 to June 2018, enrolling studies reporting short- or long-term outcomes of ESD in comparison with resection surgery for EGC. The quality of the studies was assessed by the Newcastle-Ottawa Quality Assessment Scale. Stata software (version 12.0) was used for the analysis. Pooling analysis was conducted using either fixed- or random-effects models depending on heterogeneity across studies. RESULTS: = 0.300) showed no significant differences between these two groups. CONCLUSION: In the treatment of EGC, ESD was safe and feasible in comparison with resection surgery, with advantages in several surgical and post-operative recovery parameters. Although the recurrence rate was higher in ESD group, the long-term survival was still comparable in these two groups, suggesting ESD could be recommended as standard treatment for EGC with indications.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.048 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".