Interleukin‐6 in Covid‐19: A systematic review and <scp>meta‐analysis</scp>
Bibliographic record
Abstract
Summary 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, we undertook a systematic review and meta‐analysis to assess the evidence in this field. We systematically searched MEDLINE and EMBASE for studies investigating the immunological response in Covid‐19; additional grey literature searches were undertaken. Study selection and data abstraction was undertaken independently by two authors. Meta‐analysis was undertaken using random effects models to compute ratios of means with 95% confidence intervals (95%CIs). Eight published studies and two preprints (n = 1798) were eligible for inclusion. Meta‐analysis of mean IL‐6 concentrations demonstrated 2.9‐fold higher levels in patients with complicated Covid‐19 compared with patients with noncomplicated disease (six studies; n = 1302; 95%CI, 1.17‐7.19; I 2 = 100%). Consistent results were found in sensitivity analyses exclusively restricted to studies comparing patients requiring ICU admission vs no ICU admission (two studies; n = 540; ratio of means = 3.24; 95%CI, 2.54‐4.14; P < .001; I 2 = 87%). Nine of ten studies were assessed to have at least moderate risk of bias. In patients with Covid‐19, IL‐6 levels are significantly elevated and associated with adverse clinical outcomes. Inhibition of IL‐6 may be a novel target for therapeutics for the management of dysregulated host responses in patients with Covid‐19 and high‐quality studies of intervention in this field are urgently required.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".