SynthesisFrontiers in pharmacology2025
Effectiveness of pharmacological treatments for COVID-19 due to SARS-CoV-2: a systematic literature review.
Synthesis in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of Nirmatrelvir/Ritonavir for Outpatients in the Era of Omicron, Vaccination, and Previous Infection: A Meta-analysis.Journal of general internal medicine · 2026Pooled it
- Anti-Inflammatory Mechanisms and Translational Relevance ofJournal of inflammation research · 2026Review
- Immunosenescence and susceptibility to respiratory viruses: a state-of-the-art review.European respiratory review : an official journal of the European Respiratory Society · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aim: Since the first cases of the COVID-19 pandemic, caused by the SARS-CoV-2 virus, described in 2019, numerous drugs have been proposed for the treatment of the disease. However, studies have given contradictory or inconclusive results, making it difficult to determine which treatments are truly effective. The objective was to carry out a systematic review of the literature analyzing the effectiveness (mortality, hospitalization and clinical improvement) of COVID-19 treatments initially proposed and finally authorized in the European Union. Methods: PubMed and other electronic databases were systematically searched for meta-analyses published between January 2020 and December 2022, as well as two additional searches: one of individual clinical studies published until October 2023 and another of those drugs that were considered at the beginning and that were discarded early because the clinical results were unfavorable. Results: In the synthesis, 85 meta-analyses and 19 additional clinical studies were included (base case). All medications indicated in the treatment of COVID-19 have favorable efficacy results (mortality, hospitalization rate, clinical improvement) but these results were not confirmed in all studies carried out, being frequently contradictory (confirming or not confirming the impact of treatment on mortality). According to meta-analysis with the largest sample size, the drugs with the greatest evidence of effectiveness in reducing mortality are remdesivir (HR= 0.79; 95% CI 0.73-0.85) and tocilizumab (OR= 0.73; 95% CI 0.56-0.93). Regarding the composite of Covid-19-related hospitalization or death from any cause, the drugs with the greatest evidence of efficacy are remdesivir, nirmatrelvir/ritonavir and sotrovimab (although, currently the effectiveness of monoclonal antibodies against the new variants of the virus has not been demonstrated). Conclusion: According to this systematic review, the treatments with the greatest evidence of reducing mortality in patients with COVID-19 are remdesivir and tocilizumab.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.