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Record W4411915892 · doi:10.1111/jsap.13910

Understanding the rationale for metronidazole use in dogs and cats

2025· article· en· W4411915892 on OpenAlexaff
J. Ng, N. Steffensen, Ian Battersby, J. Scott Weese, Dorina Timofte, Pierre‐Louis Toutain, J. Granick, Jonathan Elliott, Seong Kyu Choi, Tim H. Sparks, Selena K Tavener, Fergus Allerton

Bibliographic record

VenueJournal of Small Animal Practice · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMetronidazoleMedicineAntimicrobial stewardshipMedical prescriptionAntimicrobialAntibioticsCATSIntensive care medicineInternal medicinePharmacologyAntibiotic resistanceMicrobiology

Abstract

fetched live from OpenAlex

OBJECTIVE: It is currently unknown how often antibiotics (including metronidazole) are used for non-antibacterial purposes in dogs and cats. This study looked to characterise the rationale for metronidazole prescription in these species. METHODS: Retrospective cohort study. Veterinarians reported clinical information for dogs and cats treated with metronidazole in the previous year, including the rationale for metronidazole selection. RESULTS: Three hundred and thirty-two cases were reported by 138 veterinarians describing metronidazole use in 47 cats and 285 dogs. Metronidazole was most commonly prescribed to treat acute diarrhoea (n = 156, 47%), chronic diarrhoea (n = 79, 24%) or giardiasis (n = 36, 11%). Veterinarians selected metronidazole exclusively for non-antimicrobial targeted therapy in 42% of cases (125/300). Putative anti-inflammatory/immunomodulatory properties were cited in 64% of cases (213/332). Educational resources (41/92, 45%), team-based collaboration (29/92, 32%) and specialist consultation (10/92, 11%) were cited as the supportive basis for these prescription choices. CLINICAL SIGNIFICANCE: Veterinarians are using metronidazole frequently for non-antimicrobial properties in contradiction to antimicrobial use guidelines. Future stewardship programs should adapt guidance specifically to counter this prescribing behaviour.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.154

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.092
GPT teacher head0.301
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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