Interleukin-6 in COVID-19: A Systematic Review and Meta-Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Purpose Coronaviruses may activate dysregulated host immune responses. As exploratory studies have suggested that interleukin-6 (IL-6) levels are elevated in cases of complicated COVID-19 and that the anti-IL-6 biologic tocilizumab may be beneficial, we undertook a systematic review and meta-analysis to assess the evidence in this field. Methods We systematically searched MEDLINE and EMBASE for studies investigating the immunological response in COVID-19 or its treatment with tocilizumab; additional grey literature searches were undertaken. Meta-analysis was undertaken using random effects models. Results Eight published studies, three pre-prints, and five registered trials were eligible. Meta-analysis of mean IL-6 concentrations demonstrated 2.9-fold higher levels in patients with complicated COVID-19 compared with patients with non-complicated disease (six studies; n=1302; 95%CI, 1.17-7.19; I 2 =100%). A single non-randomized, single-arm study assessed tocilizumab in patients with severe COVID-19, demonstrating decreased oxygen requirements, resolution of radiographic abnormalities, and clinical improvement. No adverse events or deaths were observed. Conclusions In patients with COVID-19, IL-6 levels are significantly elevated and associated with adverse clinical outcomes. While inhibition of IL-6 with tocilizumab appears to be efficacious and safe in preliminary investigation, the results of several ongoing clinical trials should be awaited to better define the role of tocilizumab in COVID-19 prior to routine clinical application. PROSPERO Registration CRD42020175879
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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 | 0.005 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.025 | 0.005 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it