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Record W4387439311 · doi:10.1161/cir.0000000000001184

Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association

2023· review· en· W4387439311 on OpenAlexfundno aff
Chiadi E. Ndumele, Janani Rangaswami, Sheryl L. Chow, Ian J. Neeland, Katherine R. Tuttle, Sadiya S. Khan, Josef Coresh, Roy O. Mathew, Carissa M. Baker‐Smith, Mercedes R. Carnethon, Jean‐Pierre Després, Jennifer E. Ho, Joshua J. Joseph, Walter N. Kernan, Amit Khera, Mikhail Kosiborod, Carolyn L. Lekavich, Eldrin F. Lewis, Kevin Bryan Lo, Bige Ozkan, Latha Palaniappan, Sonali S. Patel, Michael Pencina, Tiffany M. Powell‐Wiley, Laurence Sperling, Salim S. Virani, Jackson T. Wright, Radhika Rajgopal Singh, Mitchell S.V. Elkind

Bibliographic record

VenueCirculation · 2023
Typereview
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsnot available
FundersJanssen PharmaceuticalsNational Heart, Lung, and Blood InstituteCenters for Disease Control and PreventionNational Institutes of HealthNational Center for Advancing Translational SciencesAgencja Badań MedycznychMcGill University Health CentreMcGill UniversityNational Institute of Neurological Disorders and StrokeNovo NordiskHealth Services Research and DevelopmentUniversity of WashingtonU.S. Department of Veterans AffairsPatient-Centered Outcomes Research InstituteAstraZenecaEli Lilly and CompanyEmory University
KeywordsMedicineKidney diseaseMetabolic syndromeIntensive care medicineDiseasePopulationSubspecialtyKidneyInternal medicineObesityEnvironmental healthPathology

Abstract

fetched live from OpenAlex

Cardiovascular-kidney-metabolic health reflects the interplay among metabolic risk factors, chronic kidney disease, and the cardiovascular system and has profound impacts on morbidity and mortality. There are multisystem consequences of poor cardiovascular-kidney-metabolic health, with the most significant clinical impact being the high associated incidence of cardiovascular disease events and cardiovascular mortality. There is a high prevalence of poor cardiovascular-kidney-metabolic health in the population, with a disproportionate burden seen among those with adverse social determinants of health. However, there is also a growing number of therapeutic options that favorably affect metabolic risk factors, kidney function, or both that also have cardioprotective effects. To improve cardiovascular-kidney-metabolic health and related outcomes in the population, there is a critical need for (1) more clarity on the definition of cardiovascular-kidney-metabolic syndrome; (2) an approach to cardiovascular-kidney-metabolic staging that promotes prevention across the life course; (3) prediction algorithms that include the exposures and outcomes most relevant to cardiovascular-kidney-metabolic health; and (4) strategies for the prevention and management of cardiovascular disease in relation to cardiovascular-kidney-metabolic health that reflect harmonization across major subspecialty guidelines and emerging scientific evidence. It is also critical to incorporate considerations of social determinants of health into care models for cardiovascular-kidney-metabolic syndrome and to reduce care fragmentation by facilitating approaches for patient-centered interdisciplinary care. This presidential advisory provides guidance on the definition, staging, prediction paradigms, and holistic approaches to care for patients with cardiovascular-kidney-metabolic syndrome and details a multicomponent vision for effectively and equitably enhancing cardiovascular-kidney-metabolic health in the population.

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)0.021
metaresearch head score (Gemma)0.032
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: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.032
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.001
Science and technology studies0.0050.002
Scholarly communication0.0110.006
Open science0.0040.004
Research integrity0.0250.037
Insufficient payload (model declined to judge)0.0350.024

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 head0.066
GPT teacher head0.348
Teacher spread0.283 · 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

Citations1,653
Published2023
Admission routes1
Has abstractyes

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