Management of colorectal laterally spreading tumors: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Objective and study aims To evaluate the efficacy and safety of different endoscopic resection techniques for laterally spreading colorectal tumors (LST). Methods Relevant studies were identified in three electronic databases (PubMed, ISI and Cochrane Central Register). We considered all clinical studies in which colorectal LST were treated with endoscopic resection (endoscopic mucosal resection [EMR] and/or endoscopic submucosal dissection [ESD]) and/or transanal minimally invasive surgery (TEMS). Rates of en-bloc/piecemeal resection, complete endoscopic resection, R0 resection, curative resection, adverse events (AEs) or recurrence, were extracted. Study quality was assessed with the Newcastle-Ottawa Scale and a meta-analysis was performed using a random-effects model. Results Forty-nine studies were included. Complete resection was similar between techniques (EMR 99.5 % [95 % CI 98.6 %-100 %] vs. ESD 97.9 % [95 % CI 96.1 – 99.2 %]), being curative in 1685/1895 (13 studies, pooled curative resection 90 %, 95 % CI 86.6 – 92.9 %, I2 = 79 %) with non-significantly higher curative resection rates with ESD (93.6 %, 95 % CI 91.3 – 95.5 %, vs. 84 % 95 % CI 78.1 – 89.3 % with EMR). ESD was also associated with a significantly higher perforation risk (pooled incidence 5.9 %, 95 % CI 4.3 – 7.9 %, vs. EMR 1.2 %, 95 % CI 0.5 – 2.3 %) while bleeding was significantly more frequent with EMR (9.6 %, 95 % CI 6.5 – 13.2 %; vs. ESD 2.8 %, 95 % CI 1.9 – 4.0 %). Procedure-related mortality was 0.1 %. Recurrence occurred in 5.5 %, more often with EMR (12.6 %, 95 % CI 9.1 – 16.6 % vs. ESD 1.1 %, 95 % CI 0.3 – 2.5 %), with most amenable to successful endoscopic treatment (87.7 %, 95 % CI 81.1 – 93.1 %). Surgery was limited to 2.7 % of the lesions, 0.5 % due to AEs. No data of TEMS were available for LST. Conclusions EMR and ESD are both effective and safe and are associated with a very low risk of procedure related mortality.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".