Evaluation of Ivermectin as a Potential Treatment for Mild to Moderate COVID-19: A Double-Blind Randomized Placebo Controlled Trial in Eastern India
Bibliographic record
Abstract
BACKGROUND: There has been a growing interest in ivermectin ever since it was reported to have an in-vitro activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This trial was conducted to test the efficacy of ivermectin in mild and moderate coronavirus disease 19 (COVID-19). METHODS: A double blind, parallel, randomised, placebo-controlled trial conducted among adult COVID-19 patients with mild to moderate disease severity on admission in a COVID dedicated tertiary healthcare of eastern India. Enrolment was done between 1st August and 31st October 2020. On day 1 and 2 post enrolment, patients in the intervention arm received ivermectin 12 mg while the patients in the non-interventional arm received placebo tablets. RESULTS: About one-fourth (23.6%) of the patients in the intervention arm and one-third (31.6%) in the placebo arm were tested reverse transcriptase polymerase chain reaction (RTPCR) negative for SARS-CoV-2 on 6th day. Although this difference was found to be statistically insignificant [rate ratio (RR): 0.8; 95% confidence interval (CI): 0.4-1.4; p=0.348]. All patients in the ivermectin group were successfully discharged. In comparison the same for the placebo group was observed to be 93%. This difference was found to be statistically significant (RR: 1.1; 95% CI; 1.0-1.2; p=0.045). CONCLUSIONS: Inclusion of ivermectin in treatment regimen of mild to moderate COVID-19 patients could not be said with certainty based on our study results as it had shown only marginal benefit in successful discharge from the hospital with no other observed benefits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".