Prognostic Influence of Increased C-Reactive Protein and Fibrinogen Levels in Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The prognostic influences of fibrinogen and C-reactive protein (CRP) levels and their relations in ischemic stroke have not been well described. The aim of this study was to investigate and compare the 1-year prognostic influences of fibrinogen and CRP levels on outcome in ischemic stroke. METHODS: Fibrinogen and CRP were determined within 24 hours after stroke and related to 1-year outcome in 128 patients with first-ever ischemic stroke. The Kaplan-Meier technique was applied in survival analysis. Multiple logistic regression analysis was used to evaluate the associations between risk factors and outcome. RESULTS: The probabilities of death or new vascular event were 21.1%, 27.9%, and 51.7% (P:=0.0172, chi(2) for trend), respectively, in patients stratified by tertiles of fibrinogen (<3.78, 3.78 to 6.17, and >6.17 g/L). The probabilities of a primary end point were 12.1%, 29.7%, and 54.8% (P:=0.0004), respectively, after stratification of patient data by tertiles of CRP level (<5, 5 to 33, and >33 mg/L). In multiple logistic regression analysis, higher CRP levels (odds ratio, 2.39; 95% CI, 1.28 to 4.49; P:=0.0066) and stroke severity on the Canadian Neurological Stroke Scale (odds ratio, 2.37; 95% CI, 1.01 to 5.58; P:=0.0472) were independently associated with death or new vascular event. CONCLUSIONS: Increased levels of CRP are associated with a worse outcome in patients with ischemic stroke. The increased risk associated with elevated CRP levels is independent of the prognostic influence of fibrinogen.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".