Medication use during COVID-19
Bibliographic record
Abstract
OBJECTIVE: To keep health care providers, in response to the ongoing coronavirus disease 2019 (COVID-19) pandemic, informed about the medications that have been proposed to treat the disease and the evidence supporting their use. QUALITY OF EVIDENCE: , as well as related terms. Relevant research studies conducted in human populations and cases specific to patients with COVID-19 were included, as were relevant hand-searched papers and reviews. Only articles in English and Chinese were included. MAIN MESSAGE: While current management of patients with COVID-19 largely involves supportive care, without a widely available vaccine, practitioners have also resorted to repurposing medications used for other indications. This has caused considerable controversy, as many of these treatments have limited clinical evidence supporting their use and therefore pose implications for patient safety, drug access, and public health. For instance, medications such as hydroxychloroquine and chloroquine, lopinavir-ritonavir, nonsteroidal anti-inflammatory drugs, angiotensin-converting enzyme inhibitors, and angiotensin receptor blockers gained widespread media attention owing to hype, misinformation, or misinterpretation of research evidence. CONCLUSION: Given the severity of the pandemic and the potential broad effects of implementing safe and effective treatment, this article provides a narrative review of the current evidence behind the most widely used medications to treat COVID-19 in order to enable health care practitioners to make informed decisions in the care of patients with this life-threatening disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".