Immunomodulatory Therapy and Mortality in Patients with Sepsis: A Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: Sepsis, a severe infectious disease, is characterized by high mortality and significant therapeutic challenges. This study aims to systematically review the impact of immunomodulatory therapy on sepsis-related mortality and create an evidence-based foundation for sepsis treatment. METHODS: A systematic search was conducted in multiple databases including CNKI, VIP, Wanfang Data, and PubMed, with a cutoff date of November 6, 2024. The Cochrane Risk of Bias 2.0 tool was employed to evaluate the risk of bias for randomized controlled trials (RCTs), and the Newcastle-Ottawa Scale was used for non-RCTs (NRCTs). Data analyses were conducted via the R package meta, with the relative risk (RR) and 95% CI as effect sizes. Heterogeneity was evaluated using the Cochran's Q test and I2 statistic, and publication bias was judged by a funnel plot. RESULTS: A total of 1,783 articles were retrieved, and 21 articles (including 22 comparison groups) were finally included after screening, involving 19 RCTs and 2 NRCTs with a total of 5,276 patients. Meta-analysis results indicated that immunomodulatory therapy could reduce the risk of death in patients with sepsis (RR = 0.87, 95% CI: 0.81-0.93, I2 = 44%, p = 0.01). Subgroup analysis revealed that the overall heterogeneity mainly came from immunoglobulin G therapy (I2 = 63%), while the effects of afelimomab, methylprednisolone, Xuebijing, α1-thymosin (Tα1), and ulinastatin + Tα1 therapies were highly consistent with zero heterogeneity. CONCLUSION: Immunomodulatory therapy can reduce the risk of death in patients with sepsis, but there is moderate heterogeneity, and its efficacy may be affected by factors such as the type of specific immunomodulatory therapy.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.056 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| 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".