Low-Carbohydrate Diet Score and Macronutrient Intake in Relation to Survival After Colorectal Cancer Diagnosis
Bibliographic record
Abstract
Abstract Background A low-carbohydrate diet may improve cancer survival, but relevant clinical evidence remains limited. Methods We followed 1542 stages I to III colorectal cancer (CRC) patients who completed a validated food frequency questionnaire between 6 months and 4 years after diagnosis. We calculated overall, animal-, and plant-rich, low-carbohydrate diet scores and examined their associations with CRC-specific and overall mortality using Cox proportional hazards regression after adjusting for potential predictors for cancer survival. We also assessed the intake and changes of macronutrients after diagnosis. Statistical tests were two-sided. Results Although no association was found for overall and animal-rich low-carbohydrate diet score, plant-rich, low-carbohydrate diet, which emphasizes plant sources of fat and protein with moderate consumption of animal products, was associated with lower CRC-specific mortality (hazard ratio [HR] comparing extreme quartiles = 0.37, 95% confidence interval [CI] = 0.25 to 0.57, Ptrend < .001). Carbohydrate intake was associated with higher CRC-specific mortality, and this association was restricted to carbohydrate consumed from refined starches and sugars (HR per one-SD increment = 1.36, 95% CI = 1.14 to 1.62, Ptrend < .001). In contrast, replacing carbohydrate with plant fat and protein was associated with lower CRC-specific mortality, with the HR per one-SD increment of 0.81 (95% CI = 0.69 to 0.95, Ptrend = .01) for plant fat and 0.77 (95% CI = 0.62 to 0.95, Ptrend = .02) for plant protein. Similar results were obtained for overall mortality and when changes in macronutrient intake after diagnosis were assessed. Conclusion Plant-rich, low-carbohydrate diet score was associated with lower mortality in patients with nonmetastatic CRC. Substituting plant fat and protein for carbohydrate, particularly that from refined starches and sugars, may improve patients’ survival.
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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 | 0.002 |
| 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 | 0.000 |
| 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".