Effectiveness and safety of endoscopic submucosal dissection for residual or recurrent colorectal neoplasia: Meta-analysis
Bibliographic record
Abstract
Abstract Endoscopic submucosal dissection (ESD) is a potentially surgery-sparing technique for colorectal neoplasia resection. Outcomes of ESD for residual or recurrent colorectal neoplasia are not well described. This meta-analysis aimed to evaluate the effectiveness and safety of ESD in treating residual or recurrent colorectal neoplasia. We searched MEDLINE and Embase up to July 24, 2023 for studies on ESD for residual or recurrent colorectal neoplasia at prior surgery or endoscopic resection sites. The primary outcome of the meta-analysis was R0 resection; secondary outcomes included recurrence, adverse events (AEs), procedure time, and hospitalization length. Pooled effect sizes were obtained using inverse variance random effects models. Subgroup analyses were based on study location, lesion size, and endoscopist experience. From 1,133 abstracts, data from 25 observational studies were included, reporting on 863 residual or recurrent lesions treated with ESD. R0 resection was achieved in 80.7% of patients (95% confidence interval 72.7–86.7%, I2 = 81%) of patients, whereas recurrence occurred in 2.0% (0.7–5.1%, I2 = 0%). Incidence of delayed bleeding and delayed perforation were 1.8% (0.7–4.2%, I2 = 0%) and 1.9% (0.6–6.3%, I2 = 35%), respectively. The former was independent of country of study, recurrent lesion size, or endoscopist experience. Mean procedure duration was 80.4 minutes (66.6–94.2, I2 = 96%) and hospitalization length was 4.2 days (2.0–6.4, I2 = 98%). This meta-analysis suggests that ESD is effective and safe for treating residual or recurrent colorectal neoplasia after previous resection, with further prospective validation studies needed to compare ESD with other endoscopic resection methods and surgery in this context.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.052 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".