Comparison of MeSH terms and KeyWords Plus terms for more accurate classification in medical research fields. A case study in cannabis research

KeyWords Plus and Medical Subject Headings (MeSH) are widely used in bibliometric studies for topic mapping. The objective of this study is to compare the two description systems in documents about cannabis research to find the concordance between systems and establish whether there is neutrality in topic mapping. A total of 25,593 articles from 1970 to 2019 were drawn from Web of Science's Core Collection and Medline and analyzed. The tidytext library, Zipf's law, topic modeling tools, the contingency coefficient, Cramer's V, and Cohen's kappa were used. The results included 10,107 MeSH terms and 28,870 KeyWords Plus terms. The Zipf distribution of the terms was different for each system in terms of slope and specificity. The documents were classified into seven topics, and the MeSH system proved better at classification. The kappa coefficient between the two systems was 0.477 (for gamma >= 0.2); the topics related with human beings presented higher concordance. The use of KeyWords Plus for topic analyses in biomedical areas is not neutral, and this point needs to be taken into account in interpreting results.

Automated summary

By comparing the use of KeyWords Plus and Medical Subject Headings in cannabis research documents, it was found that the MeSH system performed better in classification, while KeyWords Plus was not neutral for topic analysis in the biomedical field.