Dietary Glycemic Load and Cancer Recurrence and Survival in Patients with Stage III Colon Cancer: Findings From CALGB 89803
Bibliographic record
Abstract
BACKGROUND: The influence of glycemic load and related measures on survival among colon cancer patients remains largely unknown. METHODS: We conducted a prospective, observational study of 1011 stage III colon cancer patients reporting dietary intake during and 6 months after participation in an adjuvant chemotherapy trial. We examined the influence of glycemic load, glycemic index, fructose, and carbohydrate intakes on cancer recurrence and mortality using Cox proportional hazards regression; all tests of statistical significance were two-sided. RESULTS: Stage III colon cancer patients in the highest quintile of dietary glycemic load experienced an adjusted hazard ratio (HR) for disease-free survival of 1.79 (95% confidence interval [CI] = 1.29 to 2.48), compared with those in the lowest quintile (P (trend) across quintiles <.001). Increased glycemic load was associated with similar detriments in recurrence-free (P (trend) across quintiles <.001) and overall survival (P (trend) across quintiles <.001). These associations differed statistically significant by body mass index (BMI) (P (interaction) =.01). Whereas glycemic load was not associated with disease-free survival in patients with BMI < 25kg/m(2), higher glycemic load was statistically significant associated with worse disease-free survival among overweight or obese participants (BMI ≥ 25kg/m(2); HR = 2.26; 95% CI = 1.53 to 3.32; P (trend) across quintiles <.001). Increasing total carbohydrate intake was similarly associated with inferior disease-free, recurrence-free, and overall survival (P (trend) across quintiles <.001). CONCLUSION: Higher dietary glycemic load and total carbohydrate intake were statistically significant associated with an increased risk of recurrence and mortality in stage III colon cancer patients. These findings support the role of energy balance factors in colon cancer progression and may offer potential opportunities to improve patient survival.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".