Prognostic value of serum uric acid level in patients with acute myocardial infarction
Bibliographic record
Abstract
OBJECTIVES: To study the relationship between serum uric acid level and Killip classification in pateints with acute myocardial infarction (MI), and the use of serum uric acid levels as a marker of short-term mortality.METHODS: The present study involved 50 patients with acute MI and 50 controls.Serum uric acid level was measured on days 0, 3 and 7 of MI, and compared with all clinical parameters and mortality in the enrolled subjects.RESulTS: There was a statistically significant higher serum uric acid concentration in patients with MI on the day of admission compared with controls.Patients with history of MI had higher serum uric acid levels.On all days, serum uric acid levels were higher in patients who were in a higher Killip class.Two patients who died after three days of hospital stay had a serum uric acid level >7.0 gm/dL and both were in Killip class IV.CONCluSIONS: Serum uric acid levels were higher in patients with acute MI compared with normal healthy individuals.In acute MI, patients with hyperuricemia had higher mortality.Serum uric acid levels correlated with Killip classification in patients with acute MI.Serum uric acid level can be used as a marker of short-term mortality in acute MI, and hyperuricemia may be an indicator of poor prognosis.Serum uric acid levels were elevated in acute MI patients with systemic hypertension and diabetes mellitus.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".