Heterogeneity of treatment effect of interferon-β1b and lopinavir–ritonavir in patients with Middle East respiratory syndrome by cytokine levels
Bibliographic record
Abstract
Animal and human data indicate variable effects of interferons in treating coronavirus infections according to inflammatory status and timing of therapy. In this sub-study of the MIRACLE trial (MERS-CoV Infection Treated with a Combination of Lopinavir-Ritonavir and Interferon β-1b), we evaluated the heterogeneity of treatment effect of interferon-β1b and lopinavir-ritonavir versus placebo among hospitalized patients with MERS on 90-day mortality, according to cytokine levels and timing of therapy. We measured plasma levels of 17 cytokines at enrollment and tested the treatment effect on 90-day mortality according to cytokine levels (higher versus lower levels using the upper tertile (67%) as a cutoff point) and time to treatment (≤ 7 days versus > 7 days of symptom onset) using interaction tests. Among 70 included patients, 32 received interferon-β1b and lopinavir-ritonavir and 38 received placebo. Interferon-β1b and lopinavir-ritonavir reduced mortality in patients with lower IL-2, IL-8 and IL-13 plasma concentrations but not in patients with higher levels (p-value for interaction = 0.09, 0.07, and 0.05, respectively) and with early but not late therapy (p = 0.002). There was no statistically significant heterogeneity of treatment effect according to other cytokine levels. Further work is needed to evaluate whether the assessment of inflammatory status can help in identifying patients with MERS who may benefit from interferon-β1b and lopinavir-ritonavir. Trial registration: This is a sub-study of the MIRACLE trial (ClinicalTrials.gov number, NCT02845843).
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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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".