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
Background: 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 . Methods: 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. Results: 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. Conclusions: 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.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".