Diagnostic value of soluble urokinase‐type plasminogen activator receptor in patients with acute coronary syndrome: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: In contemporary clinical practice, there is an increasing need for new clinically relevant biomarkers potentially optimizing management strategies in patients with suspected acute coronary syndrome (ACS). This study aimed to determine the diagnostic utility of soluble urokinase-type plasminogen activator receptor (suPAR) levels in individuals with suspected ACS. METHODS: A literature search was performed in Web of Science, PubMed, Scopus, and the Cochrane Central Register of Controlled Trials databases, for studies comparing suPAR levels among patients with and without ACS groups. The methodological quality of the included papers was assessed using the Newcastle-Ottawa Scale (NOS). A fixed-effects model was used if I² < 50%; otherwise, the random-effects model was performed. RESULTS: Five studies with 3417 participants were included in the meta-analysis. Pooled analysis showed that mean suPAR levels in the ACS group were statistically significantly higher than in the control group (3.56 ± 1.38 vs. 2.78 ± 0.54 ng/mL, respectively; mean difference: 1.04; 95% confidence interval: 0.64-1.44; I² = 99%; p < 0.001). CONCLUSIONS: In the context of acute coronary syndrome, suPAR is a potential biomarker for the early identification of medical conditions in individuals who are being treated in emergency rooms.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".