Comparative effectiveness and safety of 32 pharmacological interventions recommended by guidelines for coronavirus disease 2019: a systematic review and network meta-analysis combining 66 trials
Bibliographic record
Abstract
BACKGROUND: The global pandemic coronavirus disease 2019 (COVID-19) has become a major public health problem and presents an unprecedented challenge. However, no specific drugs were currently proven. This study aimed to evaluate the comparative efficacy and safety of pharmacological interventions in patients with COVID-19. METHODS: Medline, Embase, the Cochrane Library, and clinicaltrials.gov were searched for randomized controlled trials (RCTs) in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/SARS-CoV. Random-effects network meta-analysis within the Bayesian framework was performed, followed by the Grading of Recommendations Assessment, Development, and Evaluation system assessing the quality of evidence. The primary outcome of interest includes mortality, cure, viral negative conversion, and overall adverse events (OAEs). Odds ratio (OR) with 95% confidence interval (CI) was calculated as the measure of effect size. RESULTS: Sixty-six RCTs with 19,095 patients were included, involving standard of care (SOC), eight different antiviral agents, six different antibiotics, high and low dose chloroquine (CQ_HD, CQ_LD), traditional Chinese medicine (TCM), corticosteroids (COR), and other treatments. Compared with SOC, a significant reduction of mortality was observed for TCM (OR = 0.34, 95% CI: 0.20-0.56, moderate quality) and COR (OR = 0.84, 95% CI: 0.75-0.96, low quality) with improved cure rate (OR = 2.16, 95% CI: 1.60-2.91, low quality for TCM; OR = 1.17, 95% CI: 1.05-1.30, low quality for COR). However, an increased risk of mortality was found for CQ_HD vs. SOC (OR = 3.20, 95% CI: 1.18-8.73, low quality). TCM was associated with decreased risk of OAE (OR = 0.52, 95% CI: 0.38-0.70, very low quality) but CQ_HD (OR = 2.51, 95% CI: 1.20-5.24) and interferons (IFN) (OR = 2.69, 95% CI: 1.02-7.08) vs. SOC with very low quality were associated with an increased risk. CONCLUSIONS: COR and TCM may reduce mortality and increase cure rate with no increased risk of OAEs compared with standard care. CQ_HD might increase the risk of mortality. CQ, IFN, and other antiviral agents could increase the risk of OAEs. The current evidence is generally uncertain with low-quality and further high-quality trials are needed.
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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 | 0.027 | 0.054 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.053 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".