S2281 Endoscopic Submucosal Dissection Versus Trans-Anal Endoscopic Surgery for Rectal Tumors: An Updated Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Colorectal cancer, particularly rectal cancer, poses significant mortality risks globally. In our systematic review and meta-analysis, we investigate the efficacy and safety of Endoscopic submucosal dissection (ESD) versus trans-anal endoscopic microsurgery (TES) for managing early-stage rectal tumors. Methods: Systematic literature retrieval was performed in PubMed, Web of Science, Scopus, and Embase from inception to April 2025. Risk of bias assessment was performed by using the Newcastle Ottawa Scale (NOS) for observational studies and Cochrane tool for assessing risk of bias in randomized trials (ROB2). Data-analysis was conducted using R version 4.2.2 (2022-10-31) and RStudio version 2022.07.2 (2009-2022, RStudio, Inc.). Results: A total of 14 studies involving 1,312 patients were analyzed, revealing comparable rates of recurrence, en bloc resection, and complication rates (bleeding and perforation) between ESD and TES. Notably, ESD demonstrated a significantly higher R0 resection rate (OR: 0.51, 95% CI: 0.30–0. 88; P = 0.0160) and shorter hospital stays compared to TES (MD: -1.22, 95% CI: -2.10– -0.35; P = 0.0063). Subgroup analyses indicated that tumor type influences outcomes, with TES showing superiority for neuroendocrine tumors. Conclusion: Despite the findings supporting current guidelines for both techniques, variations in surgical protocols and observational study designs raise concerns about biases and generalizability. Ultimately, the choice of technique should be tailored to individual tumor and patient characteristics, emphasizing the need for personalized treatment strategies. Future prospective randomized controlled trials are recommended to establish standardized protocols and long-term efficacy.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.005 | 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".