Clinical laboratory parameters and fatality of Severe fever with thrombocytopenia syndrome patients: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne infectious disease with high case fatality rate. Unfortunately, no vaccine or antiviral specifically targeting SFTS virus (SFTSV) are available for the time being. Our objective was to investigate the association between clinical laboratory parameters and fatality of SFTS patients. METHODS: The systematic review was conducted in accordance with The Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. We searched (from inception to 24th February 2022) Web of Science, PubMed, National Knowledge Infrastructure databases and Wan Fang Data for relevant researchers on SFTS. Studies were eligible if they reported on laboratory parameters of SFTS patients and were stratified by clinical outcomes. A modified version of Newcastle-Ottawa scale was used to evaluate the quality of included studies. Standardized mean difference (SMD) was used to evaluate the association between laboratory parameters and outcomes. The between-study heterogeneity was evaluated quantitatively by standard Chi-square and the index of heterogeneity (I2). Heterogeneity was explored by subgroup and sensitivity analyses, and univariable meta-regression. Publication bias was determined using funnel plots and Egger's test. RESULTS: We identified 34 relevant studies, with over 3300 participants across three countries. The following factors were strongly (SMD>1 or SMD<-0.5) and significantly (P<0.05) associated mortality: thrombin time (TT) (SMD = 1.53), viral load (SMD = 1.47), activated partial-thromboplastin time (APTT) (SMD = 1.37), aspartate aminotransferase (AST) (SMD = 1.19), lactate dehydrogenase (LDH) (SMD = 1.13), platelet count (PLT) (SMD = -0.47), monocyte percentage (MON%) (SMD = -0.47), lymphocyte percentage (LYM%) (SMD = -0.46) and albumin (ALB) (SMD = -0.43). Alanine aminotransferase, AST, creatin phosphokinase, LDH, PLT, partial-thromboplastin time and viral load contributed to the risk of dying of SFTS patients in each subgroup analyses. Sensitivity analysis demonstrated that the results above were robust. CONCLUSIONS/SIGNIFICANCE: The abnormal levels of viral load, PLT, coagulation function and liver function, significantly increase the risk of SFTS mortality, suggesting that SFTS patients with above symptoms call for special concern.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".