Inflammatory Markers and Risk of Cerebrovascular Events in Patients Initiating Dialysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Stroke remains a leading cause of morbidity and mortality for patients on dialysis; however, its risk factors in this population and measures to prevent it are not well understood. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We investigated whether inflammation was associated with cerebrovascular events in a national US cohort of 1041 incident dialysis patients enrolled from October 1995 to June 1998 and followed until January 31, 2004. Incident cerebrovascular events were defined as nonfatal (hospitalized stroke, carotid endarterectomy) and fatal (stroke death) events after dialysis initiation. With Cox proportional hazards regression analysis accounting for the competing risk of nonstroke death, we assessed the independent event risk associated with baseline levels of multiple inflammatory markers (high-sensitivity C-reactive protein [hsCRP], interleukin-6 (IL-6), matrix metalloproteinase-3 [MMP-3], and P-selectin) and hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase inhibitor (statin) use, which may have pleiotropic inflammatory effects. RESULTS: 165 patients experienced a cerebrovascular event during 3548 person-years of follow-up; overall incidence rate was 4.9/100 person-years. None of the inflammatory markers were associated with cerebrovascular event risk (adjusted hazard ratios [HRs] per log unit [95% confidence interval]: hsCRP, 0.97 [0.85 to 1.11]; IL-6, 1.04 [0.85 to 1.26]; MMP-3, 1.02 [0.70 to 1.48]; P-selectin, 0.98 [0.57 to 1.68]). Statin use was also not associated with significant risk of events in unadjusted (HR 1.07 [0.69 to 1.68]) or propensity-score adjusted analyses (HR 0.98 [0.61 to 1.56]). CONCLUSIONS: In conclusion, neither inflammatory markers nor statin use was associated with risk of cerebrovascular events. Further studies are needed to understand the pathophysiology and prevention of stroke in patients on dialysis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".