C-Reactive Protein as a Prognostic Marker After Lacunar Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Inflammatory biomarkers predict incident and recurrent cardiac events, but their relationship to stroke prognosis is uncertain. We hypothesized that high-sensitivity C-reactive protein (hsCRP) predicts recurrent ischemic stroke after recent lacunar stroke. METHODS: Levels of Inflammatory Markers in the Treatment of Stroke (LIMITS) was an international, multicenter, prospective ancillary biomarker study nested within Secondary Prevention of Small Subcortical Strokes (SPS3), a phase III trial in patients with recent lacunar stroke. Patients were assigned in factorial design to aspirin versus aspirin plus clopidogrel, and higher versus lower blood pressure targets. Patients had blood samples collected at enrollment and hsCRP measured using nephelometry at a central laboratory. Cox proportional hazard models were used to calculate hazard ratios (HRs) and 95% confidence intervals (95% CIs) for recurrence risks before and after adjusting for demographics, comorbidities, and statin use. RESULTS: Among 1244 patients with lacunar stroke (mean age, 63.3±10.8 years), median hsCRP was 2.16 mg/L. There were 83 recurrent ischemic strokes (including 45 lacunes) and 115 major vascular events (stroke, myocardial infarction, and vascular death). Compared with the bottom quartile, those in the top quartile (hsCRP>4.86 mg/L) were at increased risk of recurrent ischemic stroke (unadjusted HR, 2.54; 95% CI, 1.30-4.96), even after adjusting for demographics and risk factors (adjusted HR, 2.32; 95% CI, 1.15-4.68). hsCRP predicted increased risk of major vascular events (top quartile adjusted HR, 2.04; 95% CI, 1.14-3.67). There was no interaction with randomized antiplatelet treatment. CONCLUSIONS: Among recent lacunar stroke patients, hsCRP levels predict the risk of recurrent strokes and other vascular events. hsCRP did not predict the response to dual antiplatelets. CLINICAL TRIAL REGISTRATION URL: http://www.clinicaltrials.gov. Unique identifier: NCT00059306.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".