A276 CLINCAL PREDICTORS FOR SESSILE SERRATED ADENOMA DETECTION: AN ANALYSIS OF 17,524 COLONOSCOPIES
Bibliographic record
Abstract
Adenoma detection and removal is crucial to prevent colon cancer. Although adenoma detection rate is the current benchmark, there is increasing interest in sessile serrated adenoma detection rate (SSADR) given sessile serrated adenomas (SSAs) are more difficult to identify endoscopically. To define predictors of SSA detection in a large colonoscopy cohort. We performed a prospective observational study using colonoscopy quality metrics collected by Cancer Care Ontario. All colonoscopies performed for any indication across 20 hospitals in Southwestern Ontario between April 2017 and February 2018 were identified. Data collected included patient demographics, procedural indication, bowel preparation, cecal intubation, endoscopist information, and histology of polyps removed. Cases without histology records were excluded. A multi-variable analysis was conducted to identify factors associated with SSA detection. In total, 17,524 colonoscopies (mean (SD) age = 59.6 (14.4), 53.9% female) were identified. At least one SSA was identified in 910 procedures, corresponding to a SSADR of 5.2%. On multi-variable analysis, variables independently associated with higher SSADR included increasing patient age (OR 1.02, 95% CI 1.02–1.03, p<0.001), cecal intubation (OR 3.80, 95% CI 1.87–7.71, p<0.001), use of split dose bowel preparation (OR 1.33, 95% CI 1.00–1.77, p=0.047), and very good bowel preparation quality (OR 2.48, 95% CI 1.38–4.44, p=0.002). Factors associated with lower SSADRs included non-screening colonoscopies (OR 0.55, 95% CI 0.48–0.63, p<0.001) and non-gastroenterologist endoscopist (general surgery OR 0.50, 95% CI 0.41–0.60, p<0.001; internal medicine OR 0.70, 95% CI 0.51–0.96, p=0.027; general practice OR 0.20, 95% CI 0.06–0.68, p=0.010). Modifiable factors associated with higher SSADRs include use of split dose bowel preparation, better bowel preparation quality, cecal intubation, and specialty of endoscopist. Ongoing initiatives emphasizing the importance of these variables should be encouraged. Clinical Predictors for SSA Detection None
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".