Comparison of culture methodology for the detection of methicillin-resistant <i>Staphylococcus pseudintermedius</i> in clinical specimens collected from dogs
Bibliographic record
Abstract
Methicillin-resistant Staphylococcus pseudintermedius (MRSP) has emerged as a major pathogen in dogs and has been implicated as a hospital-acquired pathogen in veterinary hospitals. We attempted to determine if selective culture methods will detect more MRSP when compared to the traditional culture methods in clinical samples from dogs in Atlantic Canada with a high risk for MRSP infection. Each sample was tested using 4 culture methods: traditional culture; mannitol salt agar with 2 μg/mL of oxacillin (MSAox); enrichment broth (EB) with MSAox; and EB with traditional culture. Detection of penicillin-binding protein 2', via latex agglutination, was used as a confirmatory test for oxacillin resistance. We analyzed 741 samples from 556 dogs between February 2013 and April 2014. The prevalence of MRSP in samples detected by any method was estimated at 13.4% (95% CI: 11.1-16.0%). When the prevalence of MRSP was determined according to culture method, EB with MSAox detected the highest prevalence (11.2% [9.1-13.7%]), followed by EB with traditional (10.8% [8.8-13.2%]), traditional (10.1% [8.1-12.5%]), and MSAox (8.9% [7.1-11.2%]). The prevalence using the traditional culture method did not differ significantly from any of the 3 selective culture methods. Culture with MSAox detected significantly fewer MRSP than either of the EB methods. The addition of EB to current methodology is recommended, particularly for patients considered at high risk for MRSP infection.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".