Fluoroquinolones in Food-Producing Animals: A Natural Language Processing Analysis of Four Decades of Published Literature

Background/Objectives: Fluoroquinolones are classified by the World Health Organization as highest-priority critically important antimicrobials, and their use in food-producing animals is a One Health concern. We aimed to map the published literature on fluoroquinolones in food-producing animals and to extract the dosing regimens and reports of resistance it contains. Methods: We screened 77,583 Scopus records (1981–2021) and applied natural language processing, combining named-entity recognition with rule-based pattern matching, to 14,417 abstracts on cattle, pigs, poultry, sheep, and goats. Extraction was validated against a blinded manual annotation of 205 records, and temporal trends and between-species differences were assessed with Mann–Kendall and Kruskal–Wallis tests. Results: Extraction achieved an overall precision of 0.80, recall of 0.70, and F1-score of 0.75. Annual publications increased steadily (Mann–Kendall τ = 0.98, p < 0.001), led by authors affiliated with institutions in the United States, China, and India. Across 531 doses from 370 records, the median reported dose was 10 mg/kg in poultry and 5 mg/kg in cattle, pigs, sheep, and goats (p < 0.001); 67% of doses came from pharmacokinetic or residue studies. The proportion of records reporting fluoroquinolone resistance rose from 13% in the 1990s to about 20% after 2000 and has since remained stable, most often for ciprofloxacin with Escherichia coli, Campylobacter, and Salmonella. Conclusions: Natural language processing can map dosing and resistance information in the veterinary literature at scale. These outputs describe research activity and reported regimens, not fluoroquinolone consumption or resistance prevalence, and are best interpreted alongside national sales and surveillance data.