Efficacy and safety of endoscopic submucosal tunnel dissection vs endoscopic submucosal dissection for early superficial upper gastrointestinal precancerous lesions and tumors: A meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: Endoscopic submucosal tunnel dissection (ESTD) is a new treatment for superficial upper gastrointestinal precancerous lesions and tumors. However, its safety and efficacy for treating these lesions is still uncertain. This meta-analysis aimed to compare the efficacy and safety of ESTD with those of endoscopic submucosal dissection (ESD) in treating early superficial upper gastrointestinal precancerous lesions and tumors. METHODS: Electronic databases, including EMBASE, PubMed, Cochrane Library and the Web of Science, were systematically searched for articles published up to 1 April 2020. The primary outcomes were complete and en bloc resection rates of the lesions. The secondary outcomes included operation time, speed of dissection, complication and recurrence rates. The Newcastle-Ottawa quality assessment scale was used to evaluate the quality of the studies. RESULTS: Seven studies were included in the final meta-analysis. The complete and en bloc resection rates of early superficial esophageal lesions as well as the en bloc resection rate of early superficial gastric lesions were similar between the ESTD and ESD groups. Compared with the ESD group, the ESTD group had a higher en bloc resection rate for early superficial gastric lesions. A faster dissection speed was observed in both superficial esophageal and gastric lesions. In gastric lesions, the operation time of the ESTD group was shorter than that of the ESD group, while it did not differ for esophageal lesions. There were no obvious differences in bleeding and perforation rates between the groups. CONCLUSION: As an endoscopic technique for treating early superficial upper gastrointestinal lesions, ESTD is as safe as and more effective than ESD, especially for gastric lesions.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.051 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".