Fibrinogen-to-Albumin Ratio and Blood Urea Nitrogen-to-Albumin Ratio in COVID-19 Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Fibrinogen-to-albumin ratio (FAR) and blood urea nitrogen-to-albumin ratio (BAR) are inflammatory biomarkers that have been associated with clinical outcomes of multiple diseases. The objective of this study is to evaluate the association of these biomarkers with the severity and mortality of COVID-19 patients. A systematic search was performed in five databases. Observational studies that reported the association between FAR and BAR values with the severity and mortality of COVID-19 patients were included. Random-effects models were used for meta-analyses, and effects were expressed as Odds Ratio (OR) and their 95% confidence intervals (CI). Publication bias was assessed using the Begg test, while the quality assessment was assessed using the Newcastle Ottawa Scale. A total of 21 studies (n = 7949) were included. High FAR values were associated with a higher risk of severity (OR: 2.41; 95% CI 1.41−4.12; p < 0.001) and mortality (OR: 2.05; 95% CI 1.66−2.54; p < 0.001). High BAR values were associated with higher risk of mortality (OR: 4.63; 95% CI 2.11−10.15; p < 0.001). However, no statistically significant association was found between BAR values and the risk of severity (OR: 1.16; 95% CI 0.83−1.63; p = 0.38). High FAR and BAR values were associated with poor clinical outcomes.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.005 | 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".