Outpacing the resistance <i>tsunami</i> : Antimicrobial stewardship in equine medicine, an overview
Bibliographic record
Abstract
Summary Antimicrobial stewardship (AMS) is the term increasingly used to describe the multiple approaches needed to sustain the efficacy of antimicrobial drugs in the face of the increasing development and spread of antimicrobial resistance in bacterial pathogens, and the global crisis in medicine that it is engendering. The concept and the practices associated with AMS continue to evolve but the general approach is a dynamic and multifaceted one of continuous improvement based on reducing, improving, monitoring and evaluating the use of antimicrobials so as to preserve their future efficacy and to protect human and animal health. Using many equine examples, this basic overview discusses the multiple and interacting elements of AMS: Practice guidelines, infection control and prevention, clinical microbiology, resistance and use surveillance, dosage, pharmacokinetics and pharmacodynamics, regulation, education and owner compliance, leadership, coordination and measurement. There have been impressive advances in recent years in reporting and analysis of AMR in horses, in the scrutiny and assessment of how antimicrobial drugs are used in horses and in identification of areas for improvement including dosing, surgical prophylaxis, infection control, development of practice standards and the use of clinical microbiology. Antimicrobial stewardship is taking shape as we start to see the emergence of evidence‐based recommendations but far more is required. Containing and even rolling back AMR will need the continued engagement of practitioners, equine national and international practitioner organisations, researchers and educators in the academic community, horse owners, regulators and others.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".