Prediction of Mortality by Clinical Laboratory Parameters in Severe Fever with Thrombocytopenia Syndrome: A Meta-Analysis
Bibliographic record
Abstract
Background: This study intended to fully assess the predictive efficiency of different clinical laboratory parameters for the mortality risk in severe fever with thrombocytopenia syndrome (SFTS). Methods: We systematically searched the Web of Science, PubMed, Cochrane Library, and Embase up to 13 December 2024 for studies on the association of laboratory parameters with SFTS mortality. Two investigators were independently responsible for the study screening and data extraction, and they assessed the study quality using the Newcastle–Ottawa Scale (NOS). Stata17.0 was adopted for the meta-analyses. Results: We finally included 33 observational studies involving 9502 participants (1799 deaths and 7703 survivors). The results showed that increases in the viral load (odds ratio (OR) 1.93, 95% confidence interval (CI) 1.56–2.38), neutrophil-to-lymphocyte ratio (hazard ratio (HR) 1.31, 95% CI 1.13–1.51), neutrophil percentage (HR 1.02, 95% CI 1.01–1.03), white blood cells (HR 1.06, 95% CI 1.01–1.11), activated partial thromboplastin time (OR 1.07, 95% CI 1.04–1.09), prothrombin time (OR 1.31, 95% CI 1.03–1.65), creatine kinase-myocardial band (OR 1.01, 95% CI 1.01–1.02), and procalcitonin (HR 1.27, 95% CI 1.10–1.47) greatly increased the SFTS mortality, while decreases in the lymphocyte percentage (HR 0.96, 95% CI 0.94–0.98), platelets (HR 0.98, 95% CI 0.97–0.99), and albumin (HR 0.91, 95% CI 0.86–0.96) also greatly increased the SFTS mortality; the results were all statistically significant (p < 0.05). Conclusion: Abnormalities of laboratory parameters (e.g., viral load, blood routine, coagulation, multi-organ dysfunction, and inflammation indicators) are good predictors of SFTS mortality, which can provide valuable references in clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".