Plasma tissue factor pathway inhibitor levels in coronavirus disease 2019 patients: a systematic review and meta-analysis
Bibliographic record
Abstract
Studies have suggested a relationship between tissue factor pathway inhibitor (TFPI) and coronavirus disease 2019 (COVID-19) severity. However, there is inconsistency in the findings of the studies. To enhance comprehension of this relationship, a meta-analysis was conducted. PubMed, Web of Science, and Scopus databases were searched to identify eligible studies. The mean difference was employed as effect measures and the standardized mean difference (SMD) and the 95% confidence interval (CI) were utilized as a summary statistic. Heterogeneity was assessed through the application of the chi-square test and the I2 statistic. The included studies' quality and risk of bias were assessed using the Newcastle-Ottawa assessment scale, adapted for case-control studies. A total of six studies were included with 684 cases and healthy controls (180 healthy controls and 504 COVID-19 patients with different severity, 76 mild, 292 moderate, and 136 severe). The analysis revealed a significant increase in the TFPI level in COVID-19 patients with moderate severity compared with healthy controls (SMD = 0.95 ng/ml, 95% confidence interval (CI) 0.27, 1.63 ng/ml; I2 : 87.2%). The increased TFPI level in mild and moderate COVID-19 was not significant, SMD = 0.68 ng/ml, 95% CI -0.64 to 2.0 ng/ml; I2 92.9% and SMD = 0.62 ng/ml, 95% CI -0.62 to 1.86 ng/ml; I2 91.5%, respectively. In addition, most studies indicate an association of the increased TFPI concentrations with increased markers of inflammation, endothelial damage, and hypercoagulation. Considering the anticoagulant and anti-inflammatory roles of TFPI, its increase seems to be aimed at modulating COVID-19-induced hyper-inflammation and hyper-coagulation state. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42023437353.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".