Predictors of Colorectal Cancer After Negative Colonoscopy: A Population-Based Study
Bibliographic record
Abstract
OBJECTIVES: A higher proportion of colorectal neoplasia among women occurs in the proximal colon, which might be more frequently missed by colonoscopy. There are no data on predictors of developing colorectal cancer (CRC) after a negative colonoscopy in usual clinical practice. We evaluated gender differences and predictors of CRC occurring after a negative colonoscopy. METHODS: All individuals 40 years or older with negative colonoscopy were identified from Manitoba's provincial physicians' billing claims database. Individuals with less than 5 years of coverage by the provincial health plan, earlier CRC, inflammatory bowel disease, resective colorectal surgery, or lower gastrointestinal endoscopy were excluded. CRC risk after negative colonoscopy was compared to that in the general population by standardized incidence ratios. Cox regression analysis was performed to determine the independent predictors of CRC occurring after negative colonoscopy. RESULTS: A total of 45,985 individuals (18,606 men; 27,379 women) were followed up for 229,090 person-years. After a negative colonoscopy, men had a 40-50% lower risk of CRC diagnosis through most of the follow-up time. Risk among women was similar to that of women in the general population in the first 3 years and then was 40-50% lower. Older subject age and performance of index colonoscopy by non-gastroenterologists were independent predictors for early/missed CRC (cancers occurring within 3 years of negative colonoscopy). CONCLUSIONS: Women may have a higher rate of missed/early CRCs after negative colonoscopy. Predictors of missed/early CRCs after negative colonoscopy include older age and performance of index colonoscopy by a non-gastroenterologist.
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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 | 0.001 |
| 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) | 0.001 | 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".