Granada Statements

As has occurred in other health care areas (i.e., medicine and nursing), a group of clinical and social pharmacy practice journal editors gathered in Granada, Spain to discuss how journals could contribute to strengthening pharmacy practice as a discipline. The result of that meeting was compiled in these Granada Statements, which comprise 18 recommendations gathered into six topics: the appropriate use of terminology, impactful abstracts, the required peer reviews, journal scattering, more effective and wiser use of journal and article performance metrics, and authors’ selection of the most appropriate pharmacy practice journal to submit their work.

Confusing terminology used in the abbreviation of pharmacy journal names

The lack of commonly agreed terminology in pharmacy field is highly prevalent and may have influence on the relevance and robustness of the area, especially how others see pharmacy literature. Potential consequences of this poor perception of pharmacy field by the National Library of Medicine (NLM) could be the omission of several pharmacy-related Medical Subject Headings (MeSH) or the low indexing rate of pharmacy practice journals in MEDLINE. Journal name abbreviation, under the responsibility of the NLM, is the unambiguous way to identify a journal in bibliographic references and catalogs. The present study investigated the consistency of pharmacy journal abbreviations in the NLM Catalog. For the 290 journals containing any word with the root pharm in their names, a consistent procedure for NLM title abbreviations was found for 27 of the words in journal names but not for the abbreviation “Pharm”, which represented several words with very different meanings: pharmaceutical, pharmaceutics, pharmacists, and pharmacy. The use by the NLM of different abbreviation for pharmaceutical and pharmaceutics would increase journal identification clarity.

Publication: DOI: 10.1016/j.sapharm.2022.01.003

Check further information on: https://pubmed.ncbi.nlm.nih.gov/35027305/

Prediction models: the role of artificial intelligence in the COVID-19 era

A recent article implemented and evaluated different models including artificial neural networks (ANN), decision trees (DT), partial least squares discriminant analysis (PLS-DA), and K nearest neighbour algorithm (KNN)  to  predict COVID-19′ positivity and severity based on patients’ laboratory tests results. Take a look on the results in Comput Biol Med 2021 May 29;134:104531.