Abstract WP160: Inflammatory Markers and Outcomes After Lacunar Stroke: the Levels of Inflammatory Markers in Treatment of Stroke Study
Bibliographic record
Abstract
Background: C-reactive protein predicts prognosis after stroke, but relationships of other inflammatory biomarkers to prognosis is uncertain. We hypothesized that concentrations of interleukin 6 (IL6), serum amyloid A, tumor necrosis factor-α receptor 1 (TNFR1), CD40 ligand, and monocyte chemoattractant protein 1 predict recurrent major vascular events (MVE) after lacunar stroke. Methods: Levels of Inflammatory Markers in the Treatment of Stroke (LIMITS) was an international, multicenter, ancillary biomarker study nested within the Secondary Prevention of Small Subcortical Strokes (SPS3) Phase 3 trial in patients with recent lacunar stroke. Patients were randomized to aspirin versus aspirin/clopidogrel. Blood samples were collected at enrollment, and markers measured centrally using ELISA. Cox proportional hazards models were used to calculate hazard ratios and 95% confidence intervals (HR, 95% CI) for risk of MVE (stroke, myocardial infarction, vascular death) after adjusting for demographics, comorbidities, and statin use. Results: Among 1244 lacunar stroke patients (mean age 63.3 ± 10.8 years), there were 115 MVE. Risk increased with concentrations of both TNFR1 (adj HR per standard deviation [SD] 1.21, 95% CI 1.05-1.41) and IL6 (adj HR per SD 1.10, 95% CI 1.02-1.19). Compared with the bottom quartile of TNFR1, those in the top quartile had twice the risk after adjusting for demographics (HR 1.98, 95% CI 1.11-3.52), though this attenuated after adjusting for other risk factors (adjusted HR 1.68, 95% CI 0.93-3.04). There was an interaction between antiplatelet assignment and TNFR1 (p=0.008; figure) and IL6 quartiles (p=0.035); as biomarker concentrations increased, dual antiplatelets became less effective than aspirin alone. Other markers were not associated with prognosis. Conclusions: Among recent lacunar stroke patients, IL6 and TNF receptor concentrations predict risk of recurrent vascular events and efficacy of antiplatelet therapies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".