Hyperfibrinogenemia and Functional Outcome From Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Epidemiological studies have found strong correlations between elevated plasma fibrinogen levels and both ischemic stroke incidence and stroke mortality. Little is known about the influence of fibrinogen levels on functional stroke outcome. METHODS: Placebo data from the Stroke Treatment with Ancrod Trial (STAT) and European Stroke Treatment with Ancrod Trial (ESTAT) were analyzed. Fibrinogen levels were determined within 3 hours (STAT) or 6 hours (ESTAT) of stroke onset and at preset intervals throughout 5 days of intravenous infusions. Barthel Index scores at 90 days quantified functional outcomes. The association between initial fibrinogen levels and functional outcomes was evaluated using a multiple logistic regression analysis. RESULTS: Fibrinogen levels increased gradually over the first 24 hours from a pretreatment median value of 340 mg/dL to a 24-hour median value of 376 mg/dL. In a univariate analysis, the proportion of patients with good functional outcome decreased with increasing quartiles of initial fibrinogen levels in both STAT (36.0% to 26.2%) and ESTAT (53.8% to 24.8%). In a multifactorial analysis, the same trend was observed. Patients with initial fibrinogen levels <450 mg/dL had better outcomes in both studies; the difference (42.0% versus 21.6%) was significant in ESTAT (P=0.0006), even when corrected for age and initial stroke severity. CONCLUSIONS: The independent association of higher initial fibrinogen levels with poor outcome needs to be verified using a larger acute stroke dataset. Even in the present small populations, the apparent association of these 2 variables suggests that treatments designed to reduce fibrinogen levels could potentially be important in treating acute ischemic stroke.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".