Article in Infection and immunity, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 26% of its field
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
4 citing papers in PubMed, 5 citations in OpenAlex.
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
6 authors at 2 institutions in 1 country.
Pei-Xing XuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.ORCID 0009-0009-5372-7098
Hong-Yu RenNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Na ZhaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Xiao-Jing JinNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Bo-Hai WenPathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Tian QinNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.ORCID 0000-0001-6382-8989
National Institute for Communicable Disease Control and Prevention · CNInstitute of Microbiology · CN
Funding
Foundation for Innovative Research Groups of the National Natural Science Foundation of China 81671985Public Health Service Capability Improvement Project of the National Health Commission of the People's Republic of China 2100409034 0104205State Key Laboratory for Infectious Disease Prevention and Control of China 2022SKLID209 2019SKLID403
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.
Distribution characteristics of the · full record | OpenQuestion