Fluctuating Inflammatory Markers in Patients With Stable Ischemic Heart Disease
Bibliographic record
Abstract
BACKGROUND: C-reactive protein (CRP), a marker of inflammation, is increasingly measured to stratify coronary artery disease risk and guide clinical management. However, little is known about how inflammatory markers fluctuate over time in patients with stable ischemic heart disease. METHODS: We examined serial serum CRP values in 159 patients with histories spanning the clinical spectrum of ischemic heart disease. Two to 8 CRP measurements were made at intervals varying from 15 days to 6 years. Successive interleukin (IL)-6 values were examined in 1 subgroup. Blood samples were always taken when patients were clinically stable, in the absence of any potentially confounding inflammatory condition. RESULTS: C-reactive protein values in individual patients fluctuated considerably when examined in the following ranges: less than 1 mg/L, 1 to 3 mg/L, and greater than 3 mg/L, proposed to indicate low, average, and high risk. Sixty-four patients (40.3%) changed risk category between the first and the second measurement. Within-patient variances of CRP and IL-6 levels were 1.79 mg/L (95% confidence interval, 1.60-2.00) and 2.69 pg/mL (95% confidence interval, 2.29-3.18), respectively. The variability of CRP was consistent over different times and across clinical groups, and independent of body mass index, smoking status, medication, and clinical events. CONCLUSIONS: Relatively important fluctuations in CRP levels in patients with stable ischemic heart disease may be problematic for risk stratification and treatment monitoring. A similar IL-6 variability suggests that these patients have a dynamic inflammatory status whose kinetics may modulate acute coronary risk.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".