A122 MINIMALLY INVASIVE ENDOSCOPIC RESECTION TECHNIQUES FOR ANORECTAL JUNCTION NEOPLASIA: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract Background The management of neoplastic lesions at the anorectal junction remains debated. Endoscopic submucosal dissection (ESD) and endoscopic mucosal resection (EMR) have emerged as the primary endoscopic modalities of choice. Purpose We sought to compare the performance of ESD and EMR in resection of anorectal neoplasia. Method Two authors independently searched MEDLINE, EMBASE and Cochrane Libraries (Jan 2000 – Aug 2021) for citations evaluating the performance of endoscopic resection techniques (ESD, EMR) for lesions involving the anorectal junction (defined as within 20mm of the dentate line). The frequencies and 95% confidence intervals (95% CI) of technical success (complete removal of all neoplastic tissue at index procedure), clinically significant post-endoscopic resection bleeding (CSPEB), delayed perforation, recurrence and referral to surgery were assessed using random-effects modelling. Result(s) We included 11 studies (total 563 patients: 414 ESD, 149 EMR) of which nine were ESD and two were EMR studies. Technical success was achieved in 97.2% overall (95% CI 94.8%-98.5%, ESD 97.5% and EMR range 93.9%-98.0%). Clinically significant post-endoscopic resection bleeding occurred in 4.3% (95% CI 1.6%-11.1%, ESD 3.0% and EMR range 8.2%-11.0%). Delayed perforation was not identified. Recurrence at first screening colonoscopy occurred in 4.8% (95% CI 1.9%-11.7%, ESD 3.0% and EMR range 15.4%-18.4%). Referral to surgery for any reason occurred in 5.9% (95% CI 4.3%-8.0%, ESD 6.9%, EMR range 2.0%-3.0%). Conclusion(s) ESD and EMR demonstrate high frequencies of technical success but may have different rates of adverse events and recurrence. More studies investigating lesions at the anorectal junction should be conducted including head-to-head analyses between ESD and EMR for low-risk anorectal junction neoplasia. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.008 | 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 | 0.002 |
| 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".