Emerging Variants of Canine Enteric Coronavirus Associated with Outbreaks of Gastroenteric Disease.

A 2022 canine gastroenteritis outbreak in the United Kingdom was associated with circulation of a new canine enteric coronavirus closely related to a 2020 variant with an additional spike gene recombination. The variants are unrelated to canine enteric coronavirus-like viruses associated with human disease but represent a model for coronavirus population adaptation.

Recording of suspected adverse drug reaction reporting in veterinary free-text clinical narratives.

Objectives: To use text mining approaches to identify instances of suspected adverse drug reactions recorded in first opinion veterinary free-text clinical narratives, and to evaluate whether these were also reported to either the Veterinary Medicines Directorate or the relevant Marketing Authorisation holder in order to derive an estimate of the suspected adverse drug reaction (sADR) minimum under-reporting rate. To characterise sADR reports and explore whether particular features are associated with report submission.

Materials And Methods: Two regular expressions were developed to identify mentions of "adverse drug reactions" and "side effects" in the free-text clinical narratives of electronic health records contained within the Small Animal Veterinary Surveillance Network database.

A mixed-methods approach utilising electronic health records to examine antimicrobial prescription surrounding gastrointestinal clinical presentations in dogs and cats.

Introduction: Systemically-administered antimicrobials are often prescribed in canine and feline gastrointestinal clinical presentations. Responsible use of antimicrobials, particularly those considered Highest Priority Critically Important Antimicrobials (HPCIAs) is vital to tackle antimicrobial resistance. Although practice-level prescription guidance is available, further strategies based on a greater understanding of antimicrobial prescription at the population-level are needed.

Evaluating ChatGPT text mining of clinical records for companion animal obesity monitoring.

Background: Veterinary clinical narratives remain a largely untapped resource for addressing complex diseases. Here we compare the ability of a large language model (ChatGPT) and a previously developed regular expression (RegexT) to identify overweight body condition scores (BCS) in veterinary narratives pertaining to companion animals.

Methods: BCS values were extracted from 4415 anonymised clinical narratives using either RegexT or by appending the narrative to a prompt sent to ChatGPT, prompting the model to return the BCS information.