Serum Uric Acid: A Murderer or Bystander for Cardiac-related Mortality?
Bibliographic record
Abstract
In this issue of The Journal of Rheumatology , Colantonio, et al 1 conducted a case-cohort study from the REGARDS study to evaluate whether the association between serum uric acid (SUA) and sudden cardiac death, and between SUA and incident coronary heart disease (CHD) events, is confounded by SLC2A9 single-nucleotide polymorphisms (SNPs). Incident CHD events were the composites of nonfatal myocardial infarction (MI) or CHD deaths. The final analyzable data consisted of 840 patients with 235 sudden cardiac death and 851 incident CHD events. These patients were divided into the 3 groups by SUA tertiles. SUA was independently associated with sudden cardiac death but not with CHD events after the authors adjusted for SC2A9 SNPs on the top of baseline characteristics (age, sex, race, BMI, socioeconomic status, lifestyle choice, medical history), laboratory data (lipid profiles and C-reactive protein [CRP]), electrocardiographic left ventricular hypertrophy (LVH), and use of medication (statin, antihypertensive medication, diuretics, and allopurinol). Notably, some of the confounders were also the consequences of elevated SUA in the study.1 Previous studies have shown that SUA was associated with increased CRP,2,3 metabolic syndrome,4 hypertension,5,6 LVH,4,7 chronic kidney disease (CKD),8 and atrial fibrillation.9 In other words, the pathological effects of elevated SUA … Address correspondence to Dr. C.W. Liu, Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital Songshan Branch, National Defense Medical Center, Taipei, Taiwan 10581, No. 131, Jiankang Rd., Songshan Dist., Taipei City 105, Taiwan (ROC). Email: issac700319{at}gmail.com.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".