Risk Factors for Colorectal Cancer in Relation to Number and Size of Aberrant Crypt Foci in Humans
Bibliographic record
Abstract
Several characteristics of aberrant crypt foci (ACF) suggest that they are precursors of colorectal cancer, but the factors that promote or inhibit their growth are largely unknown. We conducted a pilot study to explore whether factors associated with risk of colorectal cancer are also associated with number or size of rectal ACF. Thirty-two U.S. veterans, ages 50 to 80 years, were recruited to undergo magnifying chromoendoscopy for imaging of rectal ACF and colonoscopy for identification of polyps or cancer. Participants completed a questionnaire on cigarette smoking, use of nonsteroidal anti-inflammatory drugs (NSAIDs), and family history of colorectal cancer. Fisher's exact test was used to assess the statistical significance of associations between colorectal cancer risk factors and characteristics of ACF. Cochran-Mantel-Haenszel statistics and polytomous regression were used to test the significance of associations adjusted for age. Participants with a history of adenoma had more ACF than those without (age-adjusted P = 0.02), but the numbers in the two groups overlapped markedly. Older participants had more (P = 0.06) and larger (P = 0.009) ACF than younger participants. No associations were identified between either ACF number or size and cigarette smoking, use of NSAIDs, or family history of colorectal cancer. These findings suggest that persons with adenomas have somewhat more rectal ACF than persons without, and that older age is a risk factor for ACF growth. Future research should be directed toward developing techniques to identify ACF that are likely to progress to cancer and the modifiable factors that promote or inhibit such progression.
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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 | 0.001 |
| 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 | 0.000 |
| 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".