Evidence map›Paper›PMID 36748391›Full record

ArticleZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences2022

SARS

Meiping Chen, Ting Guo, Zhiguo Zhou, Dong Chen, Haibo Zhou

Abstract readCase Reports
In one paragraph

Article in Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Meiping ChenDepartment of Infectious Diseases, First Hospital of Changsha, Changsha 410005. 435169355@qq.com.
Ting GuoDepartment of Respiratory and Critical Care Medicine, First Hospital of Changsha, Changsha 410005. 1478332385@qq.com.
Zhiguo ZhouDepartment of Respiratory and Critical Care Medicine, First Hospital of Changsha, Changsha 410005. zhouzhiguo1217@163.com.
Dong ChenDepartment of Medical Affairs, First Hospital of Changsha, Changsha 410005, China.
Haibo ZhouDepartment of Respiratory and Critical Care Medicine, First Hospital of Changsha, Changsha 410005.

Funding

the Program of Health Commission of Hunan Province, China 202203083818
6 · The paper itself

Abstract

We report a case of coronavirus disease 2019 (COVID-19) patient who was cured by oral administration of nirmatrelvir/ritonavir (Paxlovid). The patient was treated with Paxlovid after being first infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BA.5 variant. On the 11th day after SARS-CoV-2 nucleic acid test turned negative, SARS-CoV-2 nucleic acid test was positive again, and the threshold of nucleic acid cycle number was equivalent to that of the first infection. The results of two whole gene sequencing showed that it was the same virus strain infection, suggesting that the case was re-positive. Without specific treatment, SARS-CoV-2 nucleic acid detection in nasopharyngeal swab turned negative. It is not uncommon for Paxlovid to recover after treating COVID-19, and most of the patients can recover without specific treatment. However, it is necessary to further study the mechanism that may lead to the recovery of SARS-CoV-2.

Indexed as

COVID-19Nucleic AcidsAdministration, OralDrug CombinationsHumansLactamsLeucineNitrilesProlineRitonavirSARS-CoV-2Drug CombinationsLactamsLeucinenirmatrelvir and ritonavir drug combinationNitrilesNucleic AcidsProlineRitonavircoronavirus disease 2019nirmatrelvir/ritonavirrecurrencesevere acute respiratory syndrome coronavirus 2

Identifiers

PMID36748391
PMCPMC10930264

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.