Carbohydrates and colorectal cancer risk among Chinese in North America
Bibliographic record
Abstract
Previous studies have analyzed total carbohydrate as a dietary risk factor for colorectal cancer (CRC) but obtained conflicting results, perhaps attributable in part to the embedded potential confounder, fiber. The aim of this study was to analyze the nonfiber ("effective") carbohydrate component (eCarb) separately and to test the hypothesis that effective carbohydrate consumption is directly related to CRC risk. The data (473 cases and 1192 controls) were from a large, multicenter, case-control study of Chinese residing in North America. Multivariate logistic regression was used to perform a secondary analysis controlling for age; sex; consumption of fat, fiber, calcium, and total kilocalories; body mass (Quetelet's) index; family history; education; and years in North America. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to estimate risk among subgroups by sex and cancer site. A statistically significant positive association was observed between eCarb consumption and risk of CRC in both men (OR, 1.7 comparing highest with lowest tertile of eCarb consumption; 95% CI, 1.1-2.7) and women (OR, 2.7; 95% CI, 1.5-4.8). As expected, the ORs for total carbohydrate were somewhat lower than those for effective carbohydrate, but the differences were not large. A sex difference in risk by colorectal subsite was observed, with risk concentrated in the right colon for women (OR, 6.5; 95% CI, 2.4-18.4) and in the rectum for men (OR, 2.4; 95% CI, 1.2-4.8). These data indicate that increased eCarb and total carbohydrate consumption are both associated with increased risk of CRC in both sexes, and that among women, relative risk appears greatest for the right colon, whereas among men, relative risk appears greatest for the rectum.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".