Serrated Lesion Detection in a Population-based Colon Screening Program
Bibliographic record
Abstract
BACKGROUND: Serrated lesions give rise to 15% to 30% of all colorectal cancers, driven predominantly by the sessile serrated polyp (SSP). Fecal immunochemical test (FIT), has low sensitivity for SSPs. SSP detection rate (SSPDR) is influenced by performance of both endoscopists and pathologists, as diagnosis can be subtle both on endoscopy and histology. GOALS: To evaluate the SSPDR in a population-based screening program, and the influence of subspecialty trained pathologists on provincial reporting practices. STUDY: The colon screening program database was used to identify all FIT-positive patients that received colonoscopy between January 2014 and June 2017. Patient demographics, colonoscopy quality indicators, pathologic diagnoses, and FIT values were collected. This study received IRB approval. RESULTS: A total of 74,605 colonoscopies were included and 26.6% had at least 1 serrated polyp removed. The SSPDR was 7.0%, with 59% of the SSPs detected having a concurrent conventional adenoma. The mean FIT value for colonoscopies with only serrated lesions was less than that for colonoscopies with a conventional adenoma or colorectal cancer (P<0.0001). Centers with a gastrointestinal subspecialty pathologist diagnosed proportionally more SSPs (P<0.0001), and right-sided SSPs than centers without subspecialists. CONCLUSIONS: Serrated lesions often occur in conjunction with conventional adenomas and are associated with lower FIT values. Knowledge of the characteristics of SSPs is essential for pathologists to ensure accurate diagnosis of SSPs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".