Interferon beta-1a as a Candidate for COVID-19 Treatment; An Open-Label Single-Arm Clinical Trial
Bibliographic record
Abstract
\nIntroduction: Since December 2019, an outbreak of Covid-19 has imposed growing worldwide concern. Researchers around the world is working to find a treatment or a vaccine for the Covid-19 and different treatment approaches have been tested in this regard. Objective: This study was designed and conducted to assess the possible efficacy of Interferon beta-1a as a a safe and efficient candidate for Covid-19 treatment. Methods: This is an investigator-initiated, open label, single arm clinical trial. Twenty suspected patients with Covid-19 who were admitted from 6 to 10 March, 2020 at Sina hospital in Tehran, Iran with moderate to severe symptoms were enrolled. Patients were treated with antiviral and hydroxychloroquine combination therapy with addition of subcutaneous Interferon beta-1a for 5 consecutive days. Baseline charactristiv and findings during the course of admission and 5 days after discharge were recorded for all the patients. Results: Totally, 20 patients with suspected Covid-19, were participated in this study, of whom 12 patients (60%) were male. The median (IQ) of patients’ age was 55.5 (43-63.5). The most common symptom of the patients at onset of disease was fever. The median (Interquartile, IQ range) days of hospital stay was 5.0 (3-6) days. Only 2 cases were admitted to ICU. At the time of follow-up, 15 (94%) patients reported that they generally feel good and had oral tolerance, 1 patient had suffered from dyspnea, 5 patients had suffered from cough, none of them had experienced fever and no case of re-admission or death was reported after discharge. Conclusions: Results of current study are in favor of using Interferon beta-1a in addition to recommended antiviral treatment in Covid-19 patients.\n
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".