Inflammatory bowel disease uncovered in fecal immunochemical test positive patients in a Canadian provincial colon cancer screening program
Bibliographic record
Abstract
Inflammatory Bowel Disease (IBD) is a chronic inflammatory condition that usually affects younger adults but has a second incidence peak in the older population. Although diagnosis of IBD is driven by symptoms, some patients are asymptomatic and incidentally discovered while participating in colon screening program (CSP). We aimed to identify the incidence and outcome of IBD in fecal immunochemical test (FIT) positive patients in the British Columbia CSP. We conducted a retrospective chart review of patients who had colonoscopies for positive FIT and were found to have colitis based on endoscopic and histological assessment. Of 93,994 patients who underwent screening colonoscopy for positive FIT between 2009 and 2017, 608 (0.6%) were found to have colitis. From 11 CSP sites, 191 patients met the inclusion criteria. 58 patients (30.4%) were diagnosed with ulcerative colitis, 109 (57.1%) with Crohn’s disease (CD), and 24 (12.6%) with IBD unclassified. 124 patients (64.9%) received treatment, of which 34 (17.8%) received biologics and 4 (2.1%) required surgery. Our study demonstrated a clinically significant incidence of IBD, with novel finding of CD predominance, within a Canadian provincial CSP. Further research is needed to guide management of older patients with varying rates of IBD progression after incidental diagnosis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".