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Record W4390651608 · doi:10.1002/dmrr.3760

Chrono‐nutrition for hypertension

2024· article· en· W4390651608 on OpenAlexfundno aff
Natalie J. Bohmke, Dave L. Dixon, Danielle L. Kirkman

Bibliographic record

VenueDiabetes/Metabolism Research and Reviews · 2024
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteU.S. Department of Health and Human ServicesPublic Health Agency of CanadaCenters for Disease Control and PreventionAmerican Physiological SocietyAmerican Heart Association
KeywordsCircadian rhythmMedicineBlood pressureIntensive care medicineIntervention (counseling)PhysiologyBioinformaticsInternal medicineBiologyPsychiatry

Abstract

fetched live from OpenAlex

Despite the advancement in blood pressure (BP) lowering medications, uncontrolled hypertension persists, underscoring a stagnation of effective clinical strategies. Novel and effective lifestyle therapies are needed to prevent and manage hypertension to mitigate future progression to cardiovascular and chronic kidney diseases. Chrono-nutrition, aligning the timing of eating with environmental cues and internal biological clocks, has emerged as a potential strategy to improve BP in high-risk populations. The aim of this review is to provide an overview of the circadian physiology of BP with an emphasis on renal and vascular circadian biology. The potential of Chrono-nutrition as a lifestyle intervention for hypertension is discussed and current evidence for the efficacy of time-restricted eating is presented.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.004

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 head0.140
GPT teacher head0.371
Teacher spread0.231 · 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 designNot applicable
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

Citations10
Published2024
Admission routes1
Has abstractyes

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