MétaCan
Menu
Back to cohort
Record W3124830976 · doi:10.1016/j.clnu.2021.01.016

Fruit, vegetable, and legume intake and the risk of all-cause, cardiovascular, and cancer mortality: A prospective study

2021· article· en· W3124830976 on OpenAlexafffund
Weida Liu, Bo Hu, Mahshid Dehghan, Andrew Mente, Chuangshi Wang, Ruohua Yan, Sumathy Rangarajan, Lap Ah Tse, Salim Yusuf, Xiaoyun Liu, Yang Wang, Deren Qiang, Lihua Hu, Aiying Han, Xincheng Tang, Lisheng Liu, Wei Li, Chun‐Ming Chen, Wen­hua Zhao, Yin Lu, Jun Zhu, Yan Liang, Yi Sun, Qing Deng, Xuan Jia, He Xinye, Hongye Zhang, Jian Bo, Xingyu Wang, Xu Liu, Nan Gao, Xiulin Bai, Chenrui Yao, Xiaoru Cheng, Sidong Li, Xinyue Lang, Yibing Zhu, Liya Xie, Zhiguang Liu, Ying-juan Ren, Xi Dai, Liuning Gao, Liping Wang, Yuxuan Su, Guoliang Han, Rui Song, Zhuangni Cao, Yaya Sun, Xiangrong Li, Jing Wang, Li Wang, Ya Peng, Xiaoqing Li, Ling Li, Jia Wang, Jianmei Zou, Fan Gao, Shaofang Tian, Lifu Liu, Yongmei Li, Yanhui Bi, Xin Li, Anran Zhang, Dandan Wu, Ying Cheng, Yize Xiao, Fanghong Lu, Yindong Li, Yan Hou, Liangqing Zhang, Baoxia Guo, Xiaoyang Liao, Di Chen, Peng Zhang, Ning Li, Xiaolan Ma, Lei Rensheng, Fu Minfan, Yü Liu, Xiaojie Xing, Youzhu Yang, Shenghu Zhao, Quanyong Xiang, Jinhua Tang, Zhengrong Liu, Xiaoxia Li, Zhengting Xu, Ayoupu Aideeraili, Qian Zhao

Bibliographic record

VenueClinical Nutrition · 2021
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsHamilton Health SciencesMcMaster UniversityPopulation Health Research Institute
FundersCanadian Institutes of Health Research
KeywordsMedicineInterquartile rangeCancerLegumeIncidence (geometry)Environmental healthCause of deathProspective cohort studyProportional hazards modelDiseaseSurgeryInternal medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.068
GPT teacher head0.374
Teacher spread0.306 · 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

Citations57
Published2021
Admission routes2
Has abstractno

Explore more

Same venueClinical NutritionSame topicNutritional Studies and DietFrench-language works237,207