Prevalence and molecular characteristics of methicillin-resistant<i>Staphylococcus aureus</i>among skin and soft tissue infections in an emergency department in Guyana
Bibliographic record
Abstract
OBJECTIVE: The characteristics of staphylococcal skin and soft tissue infections (SSTIs) are poorly understood in northern South America and the Caribbean. The objectives of this study were to determine the frequency of methicillin resistance among Staphylococcus aureus isolates in an emergency department (ED) in Guyana and to identify specific molecular characteristics of these methicillin-resistant Staphylococcus aureus (MRSA) strains. METHODS: This was a cross-sectional study conducted at the main teaching hospital in Georgetown, Guyana. Eligible subjects included patients of all ages with SSTIs with obtainable purulent material. Purulent material was cultured, and S. aureus isolates were evaluated for antibiotic susceptibilities by disc diffusion. Molecular characterisation of MRSA isolates included identification of SCCmec type, assignment of genetic relatedness by rep-PCR and determination of the presence of two exotoxins, Panton-Valentine Leukocidin (PVL) and LukAB. RESULTS: Eighty-five samples were collected; of these, 47 grew S. aureus. 24 of the 47 S. aureus samples were MRSA (51%; 95% CI 37% to 65%), representing 28% of all samples. All MRSA isolates were SCCmec type IV, PVL positive, LukAB positive and were highly related to the current epidemic clone in the USA, USA300. CONCLUSIONS: Here, we demonstrate a clinically significant proportion of methicillin resistance in SSTI-associated staphylococcal isolates. Guyanese isolates were highly related to the most common community-associated strain seen in the USA, USA300. These results have important implications for empiric antibiotic therapy and infection control policies in Guyana and similar settings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".