ReviewNaunyn-Schmiedeberg's archives of pharmacology2025
Analysis of bibliometric research featured in Naunyn-Schmiedeberg's Archives of Pharmacology.
Review in Naunyn-Schmiedeberg's archives of pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study examines twelve (12) bibliometric research published in Naunyn-Schmiedeberg's Archives of Pharmacology and offers suggestions to improve future bibliometric analyses. By reviewing twelve influential studies, the research focuses on identifying trends in methodologies, databases, and bibliometric indicators. The reviewed studies span diverse topics, from the pharmacological effects of emodin to cancer therapies and neurodegenerative diseases. Common techniques, such as keyword co-occurrence analysis, citation burst detection, and country and institutional collaboration networks, were applied to explore research dynamics and emerging trends. However, a key observation is the predominant reliance on the Web of Science Core Collection as the sole database. While this database is widely recognized, the study suggests incorporating multiple databases to reduce bias and provide a more comprehensive understanding of the research landscape. Additionally, it recommends the use of various author performance indicators, such as the H-index, G-index, and M-index, to better capture an author's scholarly impact. The review also emphasizes the value of co-word analysis and citation burst detection in identifying research hotspots and thematic shifts. However, it advocates for more detailed co-word analysis by separately considering titles, abstracts, and keywords, co-words dynamics, and multi-gram analysis. This multi-layered approach could enhance the understanding of evolving research topics. The feedback provided is intended as constructive suggestions aimed at refining methodologies and fostering innovation in bibliometric studies within the field of pharmacology. In the same vein, the immense work of all authors is sincerely appreciated.
Indexed as
Identifiers
40272518What 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.