Understanding the rationale for metronidazole use in dogs and cats
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".