Could treatment with immunomodulatory agents targeting IL-1, IL-6, or JAK signalling improve outcomes in patients with severe influenza pneumonia? A systematic and narrative review
Bibliographic record
Abstract
<ns3:p> <ns3:bold>Background:</ns3:bold> Influenza is a global cause of morbidity and mortality and a significant risk for a future pandemic infection. Host hyperinflammation, similar to that seen in COVID-19, may occur in response to influenza virus pneumonia, with Janus kinase (JAK) signalling and proinflammatory cytokines Interleukin (IL)-1 and IL-6 involved. Immune modulation treatment of hospitalised and critically ill COVID-19 patients, including with IL-6 and JAK inhibitors, has been found to be beneficial. Significant interest exists in the use of immunomodulatory agents targeting these pathways in the treatment of severe influenza pneumonia <ns3:bold>.</ns3:bold> </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> We conducted a review with both systematic and narrative methods to assess whether, in patients with severe influenza pneumonia, treatment with immunomodulatory agents targeting IL-1, IL-6 or JAK signalling, in comparison to no immune modulation, is beneficial and improves clinical outcomes. </ns3:p> <ns3:p> <ns3:bold>Results:</ns3:bold> Our systematic search screened 5409 records and found no randomised controlled trials of IL-1, IL-6 or JAK immunomodulatory agents in patients with severe influenza pneumonia. To support this systematic search, we provide a narrative review of the biological rationale, previous use of these agents, including in hospitalised patients with COVID-19, and an overview of their safety profiles. </ns3:p> <ns3:p> <ns3:bold>Conclusions:</ns3:bold> Although immune modulation has proven successful in treating hospitalised and critically ill patients with COVID-19 and a biological rationale exists for testing these agents in influenza, no agents targeting IL-1, IL-6 or JAK signalling have been assessed in randomised controlled trials of patients with severe influenza pneumonia. This highlights a significant evidence gap. </ns3:p>
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.003 |
| 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".