Sugar-Sweetened Beverage Intake and Cancer Recurrence and Survival in CALGB 89803 (Alliance)
Bibliographic record
Abstract
BACKGROUND: In colon cancer patients, obesity, sedentary lifestyle, and high dietary glycemic load have been associated with increased risk of cancer recurrence. High sugar-sweetened beverage intake has been associated with obesity, diabetes, and cardio-metabolic diseases, but the influence on colon cancer survival is unknown. METHODS: We assessed the association between sugar-sweetened beverage consumption on cancer recurrence and mortality in 1,011 stage III colon cancer patients who completed food frequency questionnaires as part of a U.S. National Cancer Institute-sponsored adjuvant chemotherapy trial. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated with Cox proportional hazard models. RESULTS: Patients consuming ≥ 2 servings of sugar-sweetened beverages per day experienced an adjusted HR for disease recurrence or mortality of 1.67 (95% CI, 1.04-2.68), compared with those consuming <2 servings per month (P(trend) = 0.02). The association of sugar-sweetened beverages on cancer recurrence or mortality appeared greater among patients who were both overweight (body mass index ≥ 2 5 kg/m(2)) and less physically active (metabolic equivalent task-hours per week <18) (HR = 2.22; 95% CI, 1.29-3.81, P(trend) = 0.0025). CONCLUSION: Higher sugar-sweetened beverage intake was associated with a significantly increased risk of cancer recurrence and mortality in stage III colon cancer patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".