Clinical Outcomes of Endoscopic Submucosal Tunnel Dissection Vs Endoscopic Submucosal Dissection in Gastric Lesions: a Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background and objectives: Many studies have verified that endoscopic submucosal dissection (ESD) has prominent advantages in en bloc resection and low recurrence rate. However, ESD also has technical difficulty for some large-area gastric lesions. Endoscopic submucosal tunneling dissection (ESTD) combined the tunnel technique with the traditional ESD technique for treating gastrointestinal mucosal lesions under tunnel endoscopy. This technique has been gradually applied to the treatment of large-scale early cancer and precancerous lesions, and has achieved good results. Yet no meta-analysis has been published, so we performed this study to determine the efficacy and safety of ESTD vs ESD in gastric lesions through clinical outcomes.Methods: We performed the literature search in PubMed, Cochrane Library, Web of Science, Embase, Wanfang, and CNKI dating up to February 9, 2021. Studies comparing the clinical outcomes of ESTD and ESD in gastric lesions were enrolled. The Newcastle-Ottawa Quality Assessment Scale was used to evaluate the quality of these studies. Results: Four articles were included that involved a total of 920 patients (187 from the ESTD group and 733 from ESD group). ESTD has higher en bloc resection and R0 resection rate, faster dissection speed, and lower complication rate. The curative resection rate and recurrence rate of ESTD group is comparable with ESD group.Conclusions: ESTD technique is an effective and safe treatment procedure in gastric lesions, and may be prior to ESD for large gastric lesions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".