What Rheumatologists Should Know About Gout and Cardiovascular Disease
Bibliographic record
Abstract
We read with interest the recent article by Neogi on asymptomatic hyperuricemia1. Currently there is a great deal of interest about the link between serum uric acid (SUA) levels and cardiovascular events. In particular, it is still a matter of debate whether SUA is a predictor or even a causative factor of cardiovascular diseases (CVD). After the first observation and the inconsistent data of a number of subsequent studies, recent evidence points to hyperuricemia as a risk factor for CVD or even as an independent predictor of mortality at least in individuals at high CVD risk, such as those with preexisting cardiovascular disease, diabetic patients, stroke survivors, or hypertensive patients2. Moreover, the risk of CVD as well as of death from all … Address reprint requests to Dr. Massarotti.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.045 | 0.045 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".