ArticleScientific reports2024
Validation study for assessing COVID-19 pneumonia treatments.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
This study investigates the effectiveness of Azvudine and nirmatrelvir-ritonavir (Paxlovid) in treating COVID-19 pneumonia through an analysis of real-world clinical data. We retrospectively collected data from COVID-19 patients hospitalized at the Second Xiangya Hospital of Central South University between December 21, 2022, and January 18, 2023. Using kernel density estimation, box-and-whisker plots, and Schoenfeld residual plots, we evaluated the transition of patients to negative status and assessed factors such as age, disease severity, and treatment effects. The findings revealed that both Azvudine and Paxlovid significantly reduced recovery times, with Azvudine showing notable benefits for patients aged 50-80. Our analysis indicated that these drugs improved lung CT values and reduced disease severity in moderate cases. The Cox model demonstrated robustness in predicting outcomes, and a nomogram was developed for individualized recovery probability assessment. These results provide important insights into optimizing COVID-19 treatment and the potential of predictive models in clinical decision-making.
Indexed as
Identifiers
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.