Drug treatments for mild or moderate covid-19: systematic review and network meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To compare the effects of treatments for mild or moderate (that is, non-severe) coronavirus disease 2019 (covid-19). DESIGN: Systematic review and network meta-analysis. DATA SOURCES: Covid-19 Living Overview of Evidence Repository (covid-19 L-OVE) by the Epistemonikos Foundation, a public, living repository of covid-19 articles, from 1 January 2023 to 19 May 2024. The search also included the WHO covid-19 database (up to 17 February 2023) and six Chinese databases (up to 20 February 2021). The analysis included studies identified between 1 December 2019 and 28 June 2023. STUDY SELECTION: Randomised clinical trials in which people with suspected, probable, or confirmed mild or moderate covid-19 were allocated to drug treatment or to standard care or placebo. Pairs of reviewers independently screened potentially eligible articles. METHODS: After duplicate data abstraction, a bayesian network meta-analysis was conducted. Risk of bias was assessed by use of a modification of the Cochrane risk of bias 2.0 tool, and the certainty of the evidence using the grading of recommendations assessment, development, and evaluation (GRADE) approach. For each outcome, following GRADE guidance, drug treatments were classified in groups from the most to the least beneficial or harmful. RESULTS: Of 259 trials enrolling 166 230 patients, 187 (72%) were included in the analysis. Compared with standard care, two drugs probably reduce hospital admission: nirmatrelvir-ritonavir (25 fewer per 1000 (95% confidence interval 28 fewer to 20 fewer), moderate certainty) and remdesivir (21 fewer per 1000 (28 fewer to 7 fewer), moderate certainty). Molnupiravir and systemic corticosteroids may reduce hospital admission (low certainty). Compared with standard care, azithromycin probably reduces time to symptom resolution (mean difference 4 days fewer (5 fewer to 3 fewer), moderate certainty) and systemic corticosteroids, favipiravir, molnupiravir, and umifenovir probably also reduce duration of symptoms (moderate to high certainty). Compared with standard care, only lopinavir-ritonavir increased adverse effects leading to discontinuation. CONCLUSION: Nirmatrelvir-ritonavir and remdesivir probably reduce admission to hospital, and systemic corticosteroids and molnupiravir may reduce admission to hospital. Several medications including systemic corticosteroids and molnupiravir probably reduce time to symptom resolution. SYSTEMATIC REVIEW REGISTRATION: This review was not registered. The protocol is publicly available in the supplementary material.
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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 | 0.036 | 0.078 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".