IDDF2021-ABS-0025 Discriminating endoscopic features of sessile serrated lesions: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Sessile serrated lesion(SSL) is notorious for its malignancy potential and the difficulty to be detected and distinguished under endoscopy. This systematic review and meta-analysis aimed to evaluate endoscopic characteristics of SSL and delineate features that inform distinction between SSA/P and other types of lesions, including hyperplastic polyp (HP) and conventional adenoma. Methods MEDLINE, Embase and Cochrane Library from the inception to September 9, 2020, were searched for cohort, cross-sectional or case-control studies comparing endoscopic characteristics of SSL and other polyps. The primary outcome measure was the odds of finding specific endoscopic characteristics in patients with SSL compared with other types of polys in patients undergoing colorectal cancer screening. Results We included 74 studies from 16 countries comprising 34,535 SSL. Compared with HP, SSL was more likely to be in the right colon (OR 5.45; 95% CI 4.13-7.17; P<0.01); larger in size (>5mm vs. <=5mm OR 5.60; 95% CI 3.82-8.22; P<0.01); in Paris-0-II morphology (OR 2.33; 95%CI 1.45-3.70; P<0.01); having mucus cap (OR 8.48; 95% CI 4.86-14.8; P<0.01); vague margin under white light endoscopy (OR 2.71; 95% CI 1.88-3.92; P<0.01); having expended crypt opening (OR 5.09; 95% CI 1.87-13.9; P<0.01), varicose microvascular vessels (OR 6.17; 95% CI 1.57-24.3; P<0.01) and thick branched vessel (OR 5.17; 95% CI 1.81-15.8; P<0.01) under magnified narrow band imaging and type II-O pit pattern (OR 18.56; 95% CI 8.45-40.7; P<0.01) under magnified chromoendoscopy. Conclusions We systematically synthesised current evidence on the discriminating endoscopic features of SSL. These findings could enhance the detection of SSL in endoscopy practice.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".