S1267 Rate of Colorectal Cancer Detection Using Piecemeal Endoscopic Resection of Colorectal Polyps - A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Introduction: Colorectal cancer (CRC) is a leading cause of cancer-related mortality in the US and worldwide, highlighting the importance of early detection and intervention for improving patient outcomes. Endoscopic mucosal resection (EMR) is a well-established technique for the treatment of large colorectal polyps of ≥ 20 mm in size. However, the main drawback of this technique is adenoma recurrence. To our knowledge, there is no review conducted to evaluate the interval cancer development after EMR. The aim of our study is to investigate the rate interval of CRC detection after EMR of large colorectal polyps. Methods: We conducted a systematic review using databases like Pubmed, Embase, Scopus, and Web of Science. We included quantitative (experimental and observational) studies that enrolled adult patients undergoing EMR of colorectal polyps, published from inception to February 5, 2023.The primary outcome assessed was the pooled interval colorectal cancer detection rate, while the secondary outcome evaluated was the adenoma recurrence rate at 6 months following endoscopic piecemeal resection. The quality of the included studies was assessed using the Cochrane Risk of Bias tool for randomized controlled trials, and the Newcastle-Ottawa Scale for observational studies. Results: Our analysis included a total of 5 studies were included, consisting of 3 randomized controlled trials (RCTs) and 2 observational studies, with a total of 2694 individuals. The average polyp size across the study is 33.1 +/- 11.4 mm. The interval CRC detection rate including high-grade dysplasia and cancer ranged from 4.3 % (2.2 % - 8.5%) to 51.1 % (43.8 % - 58.3 %) with a pooled detection rate of 12.32 % (11.14 % - 13.62 %). The 6-month adenoma recurrence rate ranged from 5.1 % (2.6 % - 9.2 %) to 33.30 % (13.1 %-62.4 %), with a pooled rate for CRC recurrence is 15.2%. Conclusion: This study highlights the interval CRC detection and adenoma recurrence following piecemeal endoscopic resection of colorectal lesions. Our findings emphasize the importance of research to explore, refine and optimize endoscopic mucosal resection techniques to improve the prevention and management of colorectal cancer.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".