Stroke Prognosis: The Impact of Combined Thrombotic, Lipid, and Inflammatory Markers
Bibliographic record
Abstract
AIM: D-dimer, lipoprotein (a) (Lp(a)), and high-sensitivity C-reactive protein (hs-CRP) are known predictors of vascular events; however, their impact on the stroke prognosis is unclear. This study used data from the Third China National Stroke Registry (CNSR-III) to assess their combined effect on functional disability and mortality after acute ischemic stroke (AIS). METHODS: In total, 9,450 adult patients with AIS were enrolled between August 2015 and March 2018. Patients were categorized based on a cutoff value for D-dimer, Lp(a), and hs-CRP in the plasma. Adverse outcomes included poor functional outcomes (modified Rankin Scale (mRS score ≥ 3)) and one- year all-cause mortality. Logistic and multivariate Cox regression analyses were performed to investigate the relationship between individual and combined biomarkers and adverse outcomes. RESULTS: Patients with elevated levels of all three biomarkers had the highest odds of functional disability (OR adjusted: 2.01; 95% CI (1.47-2.74); P＜0.001) and mortality (HR adjusted: 2.93; 95% CI (1.55-5.33); P＜0.001). The combined biomarkers improved the predictive accuracy for disability (C-statistic 0.80 vs.0.79, P＜0.001) and mortality (C-statistic 0.79 vs.0.78, P=0.01). CONCLUSION: Elevated D-dimer, Lp(a), and hs-CRP levels together increase the risk of functional disability and mortality one-year post-AIS more than any single biomarker.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".