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Record W4416203783 · doi:10.12775/qs.2025.45.66515

Hyperuricemia and Cardiovascular Disease: Mechanisms, Associations, and Therapeutic Implications – A Literature Review

2025· article· en· W4416203783 on OpenAlexaff
Michał Szczepański, Natalia Dzieszko, Weronika Kalinowska, Paulina Sara Kulasza, Anna Szeliga, Tomasz Karol Książek, K Kozłowska, Piotr Mikołaj Dembicki, Anna Ewelina Francuziak

Bibliographic record

VenueQuality in Sport · 2025
Typearticle
Languageen
FieldMedicine
TopicGout, Hyperuricemia, Uric Acid
Canadian institutionsUniversity Hospital
Fundersnot available
KeywordsHyperuricemiaUric acidGoutBiomarkerCardiovascular healthCoronary heart disease

Abstract

fetched live from OpenAlex

Uric acid has emerged as a potential biomarker for cardiovascular risk. Hyperuricemia, described as an abnormally elevated concentration of uric acid in the bloodstream, has been associated with the development of cardiovascular diseases such as hypertension, atrial fibrillation, heart failure, and coronary heart disease. Recent studies have investigated the potential benefits of using uric acid-lowering therapy in slowing the progression of these diseases. In this review, we summarize current evidence on the impact of hyperuricemia on cardiovascular diseases and the effectiveness of urate-lowering therapy in affected patients.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.022
GPT teacher head0.330
Teacher spread0.308 · 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 designSystematic review
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

Citations0
Published2025
Admission routes1
Has abstractyes

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