A89 PREVALENCE AND OUTCOMES OF SERRATED POLYPOSIS SYNDROME IN TERTIARY GASTROENTEROLOGY PRACTICES: A RETROSPECTIVE ANALYSIS
Bibliographic record
Abstract
Abstract Background While serrated polyposis syndrome (SPS) is associated with an increased risk of colorectal cancer (CRC), incident CRC can be mitigated through programmatic endoscopic surveillance. Diagnosing SPS remains challenging due to physician awareness and the logistics of cumulative polyp tracking. There is a lack of data on SPS prevalence and outcomes outside of established cohorts. Aims To evaluate the prevalence and outcomes of SPS within tertiary gastroenterology practices. Methods Using a validated histopathology database, all patients with at least 1 sessile serrated lesion (SSL) removed by 1 of 11 gastroenterologists in a tertiary centre between 2013-2023 were considered for evaluation. All patients were subsequently screened for SPS based on the 2019 World Health Organization (WHO) criteria. Patients meeting SPS criteria underwent full chart review for demographic, procedural, and clinical outcomes. Analysis was based on serrated class lesions (SCLs), which included sessile serrated lesions, hyperplastic polyps, and traditional serrated adenomas. Results A total of 6117 patients had at least 1 SSL of which 178 (3%) met SPS criteria (110 WHO I, 10 WHO II, 58 WHO I/II). Median age at first colonoscopy was 62 years (IQR 54-71 years) and 115 (65%) were female. Diagnostic criteria were achieved over a median of 3 years (IQR 1-9 years) and a median of 3 colonoscopies (IQR 2-4 colonoscopies). On index colonoscopy, patients eventually meeting SPS criteria had a median of 3 SCLs (IQR 2-6 SCLs) with median largest lesion size of 11mm (IQR 5-18mm) whereas patients without SPS had a median of 1 SCL (IQR 1-2 SCLs) with median largest lesion size of 5mm (IQR 4-8mm) (p<0.001). SPS was diagnosed by the gastroenterologist in only 132 (74%) patients. SPS was managed by endoscopy and surgery in 157 (88%) and 21 (12%) respectively. Reasons for surgery included polyp burden (9, 5%), endoscopic adverse event (1, 0.5%) and CRC (11, 6%). CRC was diagnosed either before or concurrent to SPS diagnosis in 7 (63.6%). Conclusions In patients with at least 1 SSL, the prevalence of SPS was 3% and the diagnosis was recognized in 74% by the primary gastroenterologist. Increased awareness of SPS criteria alongside consideration for modified surveillance intervals based on index SCL size and number may improve patient outcomes. Funding Agencies 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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".