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Record W2913505158 · doi:10.1093/jncics/pky077

Low-Carbohydrate Diet Score and Macronutrient Intake in Relation to Survival After Colorectal Cancer Diagnosis

2018· article· en· W2913505158 on OpenAlexfundno aff
Mingyang Song, Kana Wu, Jeffrey A. Meyerhardt, Ömer Yılmaz, Molin Wang, Shuji Ogino, Charles S. Fuchs, Edward L. Giovannucci, Andrew T. Chan

Bibliographic record

VenueJNCI Cancer Spectrum · 2018
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsnot available
FundersNational Cancer InstituteNational Institutes of HealthHarvard T.H. Chan School of Public HealthGary Bennett Family FundDana-Farber Cancer InstituteNational Institute of Diabetes and Digestive and Kidney DiseasesAmerican Institute for Cancer ResearchEntertainment Industry FoundationAstraZenecaPfizerAmerican Cancer Society
KeywordsMedicineHazard ratioQuartileCarbohydrateProportional hazards modelColorectal cancerConfidence intervalInternal medicineCancer

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.015
GPT teacher head0.281
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations37
Published2018
Admission routes1
Has abstractyes

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