A cluster randomized trial of interferon ß-1a for the reduction of transmission of SARS-Cov-2: protocol for the Containing Coronavirus Disease 19 trial (ConCorD-19)
Bibliographic record
Abstract
BACKGROUND: SARS-CoV-2 infection rapidly spreads in populations due to the high rates of community transmission. Interrupting the shedding of SARS-CoV-2 may reduce the incidence of Coronavirus Disease 19 (COVID-19). Herein we provide a protocol for a cluster randomized trial that will examine the effectiveness of treatment with interferon (IFN) ß-1a compared to standard of care in limiting the transmission of SARS-CoV-2. Co-primary objectives are to determine whether IFN therapy reduces (a) the proportion of infected cases shedding SARS-CoV-2 at day 11 post randomization and (b) the incidence of transmission of SARS-CoV-2 infection from index cases to treatment-eligible household post-exposure contacts at day 11 after randomization. Secondary objectives include assessing the impact of IFN treatment on duration of viral clearance, hospitalizations and fatalities, and evaluating the safety of IFN treatment. METHODS: Three hundred and ten households, each including an index case with a recent COVID-19 diagnosis and at least one asymptomatic treatment-eligible household contact, will be randomized to receive 3 doses of 125 μg IFN ß-1a by subcutaneous administration (days 1, 6, and 11), or standard of care. All participants will be followed until day 29. DISCUSSION: The results from this trial will identify whether IFN ß treatment of mild or moderate COVID-19 cases accelerates viral clearance and prevents disease progression and whether IFN ß treatment of post-exposure contacts of COVID-19 cases reduces transmission of infection. TRIAL REGISTRATION: This trial is registered at ClinicalTrials.gov NCT04552379; date of registration September 17, 2020.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".