The Association between Inflammatory Markers in the Acute Phase of Stroke and Long-Term Stroke Outcomes: Evidence from a Population-Based Study of Stroke
Bibliographic record
Abstract
BACKGROUND: Little is known about the association between inflammatory markers in the acute stroke phase and long-term stroke outcomes. METHODS: In a population-based study of stroke with 5 years follow-up, we measured the level of serum heat shock protein 27 immunoglobulin G antibody (anti-HSP27), C-reactive protein (CRP), and pro-oxidant antioxidant balance (PAB) in the acute stroke phase. We analyzed the association between these inflammatory biomarkers and stroke outcomes (recurrence, death and disability/functional dependency) with using multivariable Cox proportional hazard models. RESULTS: Two hundred sixty-five patients with first-ever stroke were included in this study. The severity of stroke at admission, measured by National Institute of Health Score Scale was associated with serum concentration of CRP (Spearman's rank correlation coefficient rs = 0.2; p = 0.004). CRP also was associated with 1-year combined death and recurrence rate ([adjusted hazard ratio 1.06, 95% CI 1.01-1.12; p = 0.02]). However, we did not find any association between the concentrations of CRP, anti-HSP27, PAB, and 5-year death and stroke recurrence rates. None of 3 biomarkers was associated with the long-term disability rate (defined as modified Rankin Scale >2) and functional dependency (defined as Barthel Index <60). CONCLUSION: CRP has a significant direct, yet weak, correlation to the severity of stroke. In addition, the level of CRP at admission may have a clinical implication to identify those at a higher risk of death or recurrence.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".