Use of Folic Acid–Containing Supplements after a Diagnosis of Colorectal Cancer in the Colon Cancer Family Registry
Bibliographic record
Abstract
BACKGROUND: Supplement use among cancer patients is high, and folic acid intake in particular may adversely affect the progression of colorectal cancer. Few studies have evaluated the use of folic acid-containing supplements (FAS) and its predictors in colorectal cancer patients. OBJECTIVE: To assess the use of FAS, change in use, and its predictors after colorectal cancer diagnosis. DESIGN: We used logistic regression models to investigate predictors of FAS use and its initiation after colorectal cancer diagnosis in 1,092 patients recruited through the Colon Cancer Family Registry. RESULTS: The prevalence of FAS use was 35.4% before and 55.1% after colorectal cancer diagnosis (P = 0.004). Women were more likely than men to use FAS after diagnosis [odds ratio (OR), 1.47; 95% confidence interval (95% CI), 1.14-1.89], as were those consuming more fruit (P(trend) < 0.0001) or vegetables (P(trend) = 0.001), and U.S. residents (P < 0.0001). Less likely to use FAS after diagnosis were nonwhite patients (OR, 0.66; 95% CI, 0.45-0.97), current smokers (OR, 0.67; 95% CI, 0.46-0.96), and those with higher meat intake (P(trend) = 0.03). Predictors of FAS initiation after diagnosis were generally similar to those of FAS use after diagnosis, although associations with race and vegetable intake were weaker and those with exercise stronger. CONCLUSIONS: Our analysis showed substantial increases in the use of FAS after diagnosis with colorectal cancer, with use or initiation more likely among women, Caucasians, U.S. residents, and those with a health-promoting life-style. IMPACT: Studies of cancer prognosis that rely on prediagnostic exposure information may result in substantial misclassification.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".