Antimicrobial susceptibility testing in veterinary medicine: performance, interpretation of results, best practices and pitfalls
Bibliographic record
Abstract
Abstract The performance of antimicrobial susceptibility testing (AST) of bacteria and the interpretation of AST results for bacteria isolated from animals are complex tasks which must be performed using standard published methodology and overseen by experts in clinical microbiology and in consultation with clinical pharmacologists. Otherwise, AST has significant potential for errors and mistakes. In this review, we provide guidance on how to correctly perform AST of bacteria isolated from animals and interpret the AST results. Particular emphasis is placed on the various approved or published methodologies for the different bacteria as well as the application of interpretive criteria, including clinical breakpoints and epidemiological cut-off values (ECVs/ECOFFs). Application of approved interpretive criteria and definitions of susceptible, susceptible dose-dependent, nonsusceptible, intermediate, and resistant for clinical breakpoints as well as wild-type and non-wildtype for ECVs, are explained and the difficulties resulting from the lack of approved clinical breakpoints for other bacteria, indications, and animal species is discussed. The requirement of quality controls in any AST approach is also emphasized. In addition, important parameters, often used in monitoring and surveillance studies, such as MIC 50 , MIC 90 , and testing range, are explained and criteria for the classification of bacteria as multidrug-resistant, extensively drug-resistant or pandrug-resistant are provided. Common mistakes are presented and the means to avoid them are described. To provide the most accurate AST, one must strictly adhere to approved standards or validated methodologies, like those of the Clinical and Laboratory Standards Institute or other internationally accepted AST documents and the detailed information provided therein.
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 | 0.083 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".