Comparison of the Effectiveness and Safety of Different Methods of Colorectal Endoscopic Submucosal Dissection: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Traditional endoscopic submucosal dissection (ESD) has developed different methods, such as pocket method (P-ESD), traction-assisted method (T-ESD), and hybrid method (H-ESD). In this meta-analysis, the benefits and drawbacks of different ESD methods were discussed and ranked. STUDY DESIGN: Studies comparing different methods of colorectal ESD were searched by using PubMed, EMBASE, and Cochrane Library databases. The study was conducted for five endpoints: en bloc resection rate, R0 resection rate, operation time, dissection speed, and adverse events rate. Pairwise and network meta-analyses were performed through Rev Man 5.4 and Stata 16.0. The quality of all included studies was assessed using the Cochrane Risk of Bias Tool and the Newcastle-Ottawa Scale. RESULTS: Twenty-six studies met the inclusion criteria, including 7 RCTs and 19 non-RCTs, with a total of 3,002 patients. The pooled analysis showed that the en bloc resection rate of H-ESD was significantly lower than that of C-ESD, P-ESD, and T-ESD (RR = 0.28, 95% CI [0.12, 0.65]; RR = 0.11, 95% CI [0.03, 0.44]; RR = 8.28, 95% CI [2.50, 27.42]). Compared with C-ESD, the operation time of H-ESD and T-ESD was significantly shorter (MD = -21.83, 95% CI [-34.76, -8.90]; MD = -23.8, 95% CI [-32.55, -15.06]). Meanwhile, the operation time of T-ESD was also significantly shorter than that of P-ESD (MD = -18.74, 95% CI [-31.93, -5.54]). The dissection speed of T-ESD was significantly faster than that of C-ESD (MD = 6.26, 95% CI [2.29, 10.23]). CONCLUSION: P-ESD and T-ESD are probably the two best methods of colorectal ESD at present. The advantages of P-ESD are high en bloc resection rate and low incidence of adverse events. The advantages of T-ESD are rapid dissection and short operation time.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.046 |
| Bibliometrics | 0.007 | 0.007 |
| 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".