ArticlePeerJ2019
Worldwide scientific productions with immunotherapy of sepsis: a bibliometric analysis.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed, 10 citations in OpenAlex.
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- Global research trends in artificial intelligence for critical care with a focus on chord network charts: Bibliometric analysis.Medicine · 2023Review
- Artificial Intelligence in Intensive Care Medicine: Bibliometric Analysis.Journal of medical Internet research · 2022Article
- Bibliometric Analysis of Dendritic Epidermal T Cell (DETC) Research From 1983 to 2019.Frontiers in immunology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSepsis represents a significant healthcare problem worldwide and causes a high number of deaths every year but remains to be fully understood. During and after sepsis, the host immune response is complex and involves an initial excessive host inflammatory response to infection that is closely related to tissue damage and leads to organ failure. Over the past three decades, immunotherapy for sepsis has vastly improved, but in this area, the most influential articles, journals, authors, and countries have not yet been completely summarized and analyzed.
objectivePerformed a bibliometric analysis on all the articles concerning immunotherapy for sepsis from 1962 to 2019 was our objective, and we also explored the potential correlations between publications of different countries and their gross domestic product (GDP).
methodsAll articles about immunotherapy for sepsis were extracted from the Scopus database and analyzed. We also retrieved GDP data from all the countries that have published information from the World Bank.
resultsIn summary, we have retrieved 1,483 related articles from the Scopus database starting from the first publication on immunotherapy for sepsis in 1962 through March 16, 2019. Over the past decade, the number of the articles published has increased year by year to reach 866 in total, which accounts for about 58% of all publications, with 2017 being the most prolific year when 179 articles were published. The US published 604 articles (41%), followed by China (
conclusionsOur present study analyzed all types of articles concerning immunotherapy for sepsis over the past 57 years and countries with high GDP tends to make more contributions to the medical field of this field. In the meantime, these studies highlight the importance of immunotherapy in the treatment of sepsis patients. The recognition of the historical status and development trend of this field can promote inter-agency cooperation, guide future research, and ultimately provide the basis for clinical practice guidelines.
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.