Antibiotic Utilization Among People With Multiple Sclerosis in the Netherlands, 2018-2020.

Purpose: The purpose of this study was to describe the intensity and patterns of antibiotic drug use among people with multiple sclerosis (pwMS) in the Netherlands.

Methods: People with prevalent MS between 1 January 2018 and 31 December 2020 were identified using ambulatory hospital records from the PHARMO Database Network that contains routinely collected healthcare data from the Netherlands. Out-patient pharmacy dispensing data were used to assess type of antibiotic, dosage, and amounts dispensed.

A radiocarbon bomb pulse model for estimating the age of North Atlantic cetaceans.

The tusk of the male narwhal is a prolonged canine tooth, reaching a size of up to 3 m in length. The tusk erupts through the young narwhal's upper left lip and, over time, develops into an elongated structure composed of dentine growth layers with an outer coating of cementum. In this study, we utilized bomb radiocarbon (C) to estimate the ages of three narwhal tusks, which allowed us to validate the assumption that one growth layer is deposited annually in narwhal tusks.

Development and Portability of a Text Mining Algorithm for Capturing Disease Progression in Electronic Health Records of Patients With Stage IV Non-Small Cell Lung Cancer.

Purpose: The objective was to develop and evaluate the portability of a text mining algorithm for prospectively capturing disease progression in electronic health record (EHR) data of patients with metastatic non-small cell lung cancer (mNSCLC) treated with immunochemotherapy.

Methods: This study used EHR data from patients with mNSCLC receiving immunochemotherapy (between October 1, 2018, and December 31, 2022) in four Dutch hospitals. A text mining algorithm for capturing disease progression was developed in hospitals 1 and 2 and then transferred to hospitals 3 and 4 to evaluate portability.

Efficiency of computerized clinical decision support systems involving anticoagulants: A flashmob study in Dutch hospital pharmacies.

Aims: Computerized decision support systems (CDSSs) aim to prevent adverse drug events. However, these systems generate an overload of alerts that are not always clinically relevant. Anticoagulants are frequently involved in these alerts.

Assessing accuracy of ChatGPT in response to questions from day to day pharmaceutical care in hospitals.

Background: The advent of Large Language Models (LLMs) such as ChatGPT introduces opportunities within the medical field. Nonetheless, use of LLM poses a risk when healthcare practitioners and patients present clinical questions to these programs without a comprehensive understanding of its suitability for clinical contexts.

Objective: The objective of this study was to assess ChatGPT's ability to generate appropriate responses to clinical questions that hospital pharmacists could encounter during routine patient care.