By any other name: searching for the right plasma nomenclature.

Non-thermal plasma, cold plasma, and atmospheric-pressure plasma are few terms used to describe the plasma used in plasma medicine research. The resulting ambiguity hampers literature searches, confuses discussion, and complicates collaborations. To assess the full breadth of this problem, we designed a natural language processing model (NLP) that surveyed approximately 15,000 papers in response to the query "plasma medicine" indexed in PubMed between 2020-2022. Our NLP was constructed and executed using the Hugging Face transformers API and PubMed BERT pretrained model. We used this model to determine the prevalence and to assess the utility of each term for searching literature relevant to plasma medicine. The effectiveness of each term was measured by precision, the ability to discriminate relevant and irrelevant literature; and recall, the ability to retrieve relevant literature. Each term was given a combined effectiveness score of 0-1 (1 = ideal effectiveness) accounting for precision, recall, sample size, and model confidence. Our model showed that of the twelve commonly used terms analyzed, none received a combined effectiveness score over 0.025. We concluded that there is no universal term for "plasma" that provides a satisfactory representation of literature. These results highlight the need for standardization of nomenclature in plasma medicine.