Effect of Chronic Comorbidities on Follow-up Colonoscopy After Positive Colorectal Cancer Screening Results: A Population-Based Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Fecal occult blood tests (FOBTs) are colorectal cancer screening tests used to identify individuals requiring further investigation with colonoscopy. Delayed colonoscopy after positive FOBT (FOBT+) is associated with poorer cancer outcomes. We assessed the effect of comorbidity on colonoscopy receipt within 12 months after FOBT+. METHODS: Population-based healthcare databases from Ontario, Canada, were linked to assemble a cohort of 50-74-year-old individuals with FOBT+ results between 2008 and 2017. The associations between comorbidities and colonoscopy receipt within 12 months after FOBT+ were examined using multivariable cause-specific hazard regression models. RESULTS: Of 168,701 individuals with FOBT+, 80.5% received colonoscopy within 12 months. In multivariable models, renal failure (hazard ratio [HR] 0.71, 95% confidence interval [CI] 0.62-0.82), heart failure (HR 0.77, CI 0.75-0.80), and serious mental illness (HR 0.88, CI 0.85-0.92) were associated with the lowest colonoscopy rates, compared with not having each condition. The number of medical conditions was inversely associated with colonoscopy uptake (≥4 vs 0: HR 0.64, CI 0.58-0.69; 3 vs 0: HR 0.75, CI 0.72-0.78; and 2 vs 0: HR 0.87, CI 0.85-0.89). Having both medical and mental health conditions was associated with a lower colonoscopy uptake relative to no comorbidity (HR 0.88, CI 0.87-0.90). DISCUSSION: Persons with medical and mental health conditions had lower colonoscopy rates after FOBT+ than those without these conditions. Better strategies are needed to optimize colorectal cancer screening and follow-up in individuals with comorbidities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".