Proteflazid® effectiveness for prevention and treatment of acute viral respiratory infections in the conditions of COVID-19.
Bibliographic record
Abstract
The original antiviral drug Proteflazid® has been used in clinical practice since the early 2000s for the etiotropic treatment of acute respiratory viral diseases, due to its property of blocking viral RNA and DNA polymerases. Considering that at the beginning of the global COVID- 19 pandemic, caused by RNA-containing virus of SARS-CoV-2 species in 2020, the ability of the drug active substance to inhibit the activity of 3CL- protease of the SARS-CoV-2 coronavirus was shown by molecular docking and, subsequently, to confirm the property of the active substance to block the reproduction of the SARS-CoV-2 virus in cell cultures in vitro. It was extremely important to test the effectiveness of the drug Proteflazid, drops for the prevention and treatment of COVID-19 disease at "off labell use". AIM: The aim of the study was to provide a statistical assessment of the effectiveness of the drug Proteflazid®, drops in terms of COVID-19 pandemic. MATERIALS AND METHODS: The analysis has been performed including the letters-references from medical institutions from different regions of Ukraine about the effectiveness of the drug Proteflazid®, drops. Methods of statistical analysis have been focused on dynamics and structure analysis, meta-analysis, generalization, etc. RESULTS: 90 letters-references about the effectiveness of the drug Proteflazid®, drops, during the prevention and treatment of acute respiratory viral infections, including COVID-19 diseases were analyzed. The study used references that contained the most complete information. The number of deleted letters-references is 11. Lettersreferences from 79 medical institutions from different cities and regions of Ukraine were analyzed. The period of starting taking the drug by employees and patients of medical institutions began on February 27, 2020. Final information - October 01, 2020. The vast majority of letters from medical institutions indicated that medical staff were in contact with patients potentially suffering from COVID-19. This means a high risk of being infected with Coronavirus infection. Total number of patients who took Proteflazid® for preventive purpose was 8,572, including 7,444 medical workers and 1,128 ordinary patients. Indicator "Number of fatalities" for the medical institutions providing such information was "0". Total number of patients who took Proteflazid® for therapeutic purposes was 433, including 23 medical workers and 410 ordinary patients. Indicator "Number of fatalities" for the medical institutions providing such information was "0". The total number of medical personnel and patients who used Proteflazid drops for preventive and therapeutic purposes was 9005 people. CONCLUSIONS: The statistical analysis confirmed the effectiveness of the drug Proteflazid® for the prevention and treatment of COVID-19, as, when compared with official actual data, regarding the main indicators of the incidence of COVID-19: there were no fatalities; the average treatment period decreased (1.8 times); the proportion of recovered increased (at least 1.5 times); the proportion of sick medical workers in the total population of sick medical workers decreased (2.2 times); the proportion of patients with a severe course decreased (3.3 times). It can be argued that the drug Proteflazid®, drops has shown high effectiveness in the prevention and treatment of acute respiratory viral infections, including COVID-19, among medical personnel and patients.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".