Interferon Treatments for SARS-CoV-2: Challenges and Opportunities
Bibliographic record
Abstract
Interferon (IFN) therapies are used to treat a variety of infections and diseases and could be used to treat SARS-CoV-2. However, optimal use and timing of IFN therapy to treat SARS-CoV-2 is not well documented. We aimed to synthesize available evidence to understand whether interferon therapy should be recommended for treatment compared to a placebo or standard of care in adult patients. We reviewed literature comparing outcomes of randomized control trials that used IFN therapy for adults diagnosed with SARS-CoV-2 between 2019 and 2021. Data were extracted from 11 of 669 screened studies. Evidence of IFN effectiveness was mixed. Five studies reported that IFN was a better therapy than the control, four found no or minimal difference between IFN and the control, and two concluded that IFN led to worse patient outcomes than the control. Evidence was difficult to compare because of high variability in outcome measures, intervention types and administration, subtypes of IFNs used and timing of interventions. We recommend standardized indicators and reporting for IFN therapy for SARS-CoV-2 to improve evidence synthesis and generation. While IFN therapy has the potential to be a viable treatment for SARS-CoV-2, especially when combined with antivirals and early administration, the lack of comparable of study outcomes prevents evidence synthesis and uptake.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".