Association of Coagulation Disorder with the Severity and Mortality of Coronavirus Disease 2019 (COVID-19): A Meta-Analysis
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) , a significant health concern in recent years, is known for its multiple complications. Coagulation disorder is a prevalent complication among patients with COVID-19, but the association of coagulation disorder with the severity and mortality of COVID-19 is still unclear. The object of this study is to ascertain the potential association between coagulation disorder and severity and mortality of COVID-19. Methods: We conducted a systemic literature search of the CNKI, PubMed, Cochrane Library,and Web of Science databases for all relevant studies up to October 1, 2024. All the articles published were retrieved without language restriction. Meta-analysis were performed by Stata 18.0 software. The Newcastle Ottawa scale was used to assess the quality of the included studies. The funnel plot, Egger’s regression asymmetry test, and Begg’s test used to measure the bias of publications. Results: Eighteen studies comprising 2,577 COVID-19 patients were included. Six studies was associated with the mortality of COVID-19, indicating significant differences in DD (SMD: 0.81, 95% confidence interval [CI](0.48-1.14), P=0.02, I2=63.82%); APTT (SMD: 0.37, 95% confidence interval [CI](-0.12-0.86), P=0.14, I2=86.85%); PT (SMD: 0.74, 95% confidence interval [CI](0.34-1.15), P=0.00, I2=70.28%) . Twelve studies was associated with the severity of COVID-19, indicating significant differences in DD (SMD: 1.39, 95% confidence interval [CI](0.93-1.86), P=0.00, I2=93.49%); FIB (SMD: 0.63, 95% confidence interval [CI](0.30-0.96), P=0.00, I2=86.32%); PT (SMD: 0.43, 95% confidence interval [CI](0.12-0.74), P=0.01, I2=82.73%) . Conclusions: The findings confirm that coagulation disorder is associated with severity and mortality of COVID-19. Therefore, it is imperative to monitor blood coagulation indicator and administer treatments in COVID-19 to reduce the severity and mortality.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.057 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".