Recommendations for approaches to meticillin‐resistant staphylococcal infections of small animals: diagnosis, therapeutic considerations and preventative measures.
Bibliographic record
Abstract
BACKGROUND: Multiple drug resistance (MDR) in staphylococci, including resistance to the semi-synthetic penicillinase-resistant penicillins such as meticillin, is a problem of global proportions that presents serious challenges to the successful treatment of staphylococcal infections of companion animals. OBJECTIVES: The objective of this document is to provide harmonized recommendations for the diagnosis, prevention and treatment of meticillin-resistant staphylococcal infections in dogs and cats. METHODS: The authors served as a Guideline Panel (GP) and reviewed the literature available prior to September 2016. The GP prepared a detailed literature review and made recommendations on selected topics. The World Association of Veterinary Dermatology (WAVD) provided guidance and oversight for this process. A draft of the document was presented at the 8th World Congress of Veterinary Dermatology (May 2016) and was then made available via the World Wide Web to the member organizations of the WAVD for a period of three months. Comments were solicited and posted to the GP electronically. Responses were incorporated by the GP into the final document. CONCLUSIONS: Adherence to guidelines for the diagnosis, laboratory reporting, judicious therapy (including restriction of use policies for certain antimicrobial drugs), personal hygiene, and environmental cleaning and disinfection may help to mitigate the progressive development and dissemination of MDR staphylococci.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".