Prognostic Value of Fibrinogen in Acute Coronary Syndrome: A Meta-Analysis
Bibliographic record
Abstract
Background: Acute coronary syndrome (ACS) is one of the leading causes of cardiovascular mortality worldwide. Fibrinogen, a key mediator of coagulation and inflammation, remains controversial in its prognostic value. This meta-analysis aims to evaluate the predictive role of fibrinogen in ACS patient outcomes. Methods: Following PRISMA guidelines, we systematically searched PubMed, Embase, and other databases (from inception to November 2024) for cohort or case-control studies assessing the association between fibrinogen and ACS outcomes (all-cause mortality/major adverse cardiovascular events [1]). Study quality was evaluated using the Newcastle-Ottawa Scale (NOS), and pooled effect sizes were calculated using random- or fixed-effects models. Subgroup and sensitivity analyses were performed. Results: Nine studies (12,714 patients) were included. Elevated fibrinogen levels significantly increased the risk of all-cause mortality (HR = 1.51, 95% CI: 1.27-1.81, p < 0.001), but no significant association was found with MACE (HR = 3.00, 95% CI: 0.96-9.39, p > 0.05). Subgroup analysis suggested regional differences as a source of heterogeneity. Conclusion: Fibrinogen may serve as an independent predictor of all-cause mortality in ACS patients, but its prognostic value for MACE requires further investigation. Future studies should explore its potential in clinical risk stratification.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.042 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".