Antibacterial treatment for exotic species, backyard ruminants and small flocks: a narrative review highlighting barriers to effective and appropriate antimicrobial treatment
Bibliographic record
Abstract
Antimicrobial resistance is a complex One Health issue that exists in both human and veterinary medicine. To mitigate this ever-growing problem, efforts have been made to develop guidelines for appropriate antimicrobial use (AMU) across sectors. In veterinary medicine, there are notable literature gaps for proper AMU in minor species. We conducted a structured narrative review covering the years of July 2006 - July 2021 to find antimicrobial treatments for common bacterial infections in exotic (birds, rodents, reptiles, and others), small flock (chickens, turkeys, and other fowl), and backyard small ruminant (sheep and goats) species. We retrieved a total of 4728 articles, of which 21 articles met the criteria for our review. Studies were grouped according to species, syndrome, and body system affected. Other data extracted included the bacterial pathogen(s), treatment (active ingredient), and geographical origin. Body systems reported included: intra-oral (n = 4), gastrointestinal (n = 1), respiratory (n = 2), reproductive (n = 1), skin (n = 3), aural (n = 1), ocular (n = 4), and other/multisystem (n = 5). By species, our search resulted in: rabbit (n = 5), rat (n = 2), guinea pig (n = 1), chinchilla (n = 1), guinea pig and chinchilla (n = 1), avian species (n = 1), psittacine birds (n = 2), loris and lorikeets (n = 1), turtles (n = 2), lizards (n = 1), goats (n = 2) and sheep (n = 2). The results of our findings identified a distinct gap in consistent antimicrobial treatment information for commonly encountered bacterial conditions within these species. There is a persisting need for clinical trials that focus on antibacterial treatment to strengthen the evidence base for AMU within exotic, small flock, and backyard small ruminant species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".