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Record W3016226768 · doi:10.1152/physiol.00030.2019

Intermittent Fasting: Physiological Implications on Outcomes in Mice and Men

2020· article· en· W3016226768 on OpenAlexafffund
Ju Hee Lee, Navkiran Verma, Nikita Thakkar, Christy Yeung, Hoon‐Ki Sung

Bibliographic record

VenuePhysiology · 2020
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersBanting and Best Diabetes Centre, University of TorontoHospital for Sick ChildrenInstitute of Health Services and Policy ResearchNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsIntermittent fastingPhysiologyBiologyEndocrinologyInternal medicineMedicine

Abstract

fetched live from OpenAlex

Intermittent fasting (IF) is a widely practiced dietary method that encompasses periodic restriction of food consumption. Due to its protective benefits against metabolic diseases, aging, and cardiovascular and neurodegenerative diseases, IF continues to gain attention as a preventative and therapeutic intervention to counteract these chronic diseases. Although numerous animal studies have reported positive health benefits of IF, its feasibility and efficacy in clinical settings remain controversial. Importantly, since dietary interventions such as IF have systemic effects, thoroughly investigating the tissue-specific changes in animal models is crucial to identify IF's mechanism and evaluate its potential adverse effects in humans. As such, we will review and compare the outcomes and underlying mechanisms of IF in both animal and human studies. Moreover, the limitations of IF and inconsistencies between preclinical and clinical studies will be discussed to provide insight into the gaps between translating research from bench to bedside.

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)0.002
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.075
GPT teacher head0.353
Teacher spread0.278 · 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
GenreReview

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

Citations51
Published2020
Admission routes2
Has abstractyes

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