Pharmacological treatment for patients with coronavirus disease 2019: systematic review of randomized controlled trials
Bibliographic record
Abstract
Abstract Background: The best treatment for COVID-19 is not known, with numerous agents under investigation. We determined the outcomes of patients with COVID-19 treated with different pharmacological agents. Methods: In this systematic review, we searched Ovid MEDLINE, EMBASE, CINAHL, and Cochrane Central Register of Controlled Trials for studies published between 1stJanuary and 12thAugust, 2020. We included randomized controlled trials (RCTs) of patients with COVID-19 treated with any pharmacological agent and compared with a different pharmacological agent, placebo or standard of care. Results: From 6346 citations, 19 studies were included, with an overall low risk of bias. Two RCTs evaluated the use of remdesivir in laboratory-confirmed moderate-to-severe COVID-19. One study found that 10 days of remdesivir was associated with shortened recovery time. Neither found reduction in mortality. One RCT found no association of lopinavir/ritonavir with time to clinical improvement, or mortality benefit. Two RCTs of hydrochloroquine in patients with mild, early disease demonstrated no reduction in disease severity, hospitalization rate, death or viral load. Two RCTs observed no association of hydrochloroquine in hospitalized patients with mild-to-moderate disease with virological clearance, improvement in symptoms, need for respiratory support or death. One RCT showed that the use of steroids was associated with improved survival in patients with moderate-to-severe disease, especially those requiring respiratory support. Conclusions: There is evidence for the benefit of steroids in patients with moderate-to-severe disease. Remdesivir might shorten recovery time in patients hospitalized with moderate-to-severe disease. There is currently no evidence to support the use of lopinavir/ritonavir or hydrochloroquine.Study’s Registration: PROSPERO CRD42020184433.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".