Molecular epidemiology of Staphylococcus aureus in post-earthquake northern Haiti
Bibliographic record
Abstract
BACKGROUND: Knowledge of nasal carriage is important in predicting staphylococcal infection, and no information exists regarding the endemicity of Staphylococcus aureus in Haiti. METHODS: We performed a cross-sectional analysis of S. aureus nasal screening in an acute care, a subacute rehabilitation, and a community setting, with a brief medical and epidemiological history. PCR-positive S. aureus screening nasal cultures underwent molecular analysis for spa type, SCCmec type, and virulence genes (Panton-Valentine leukocidin (PVL), toxic shock syndrome toxin (TSST), and arginine catabolic mobile element (ACME)), and were evaluated for antibiotic susceptibility using commercial tests. RESULTS: Overall carriage rates of 8.4% methicillin-susceptible S. aureus (MSSA) and 2.8% methicillin-resistant S. aureus (MRSA) were identified, with a high rate of tetracycline resistance. TSST and PVL genes were identified in MSSA. MRSA isolates contained no virulence markers. Unique MSSA phenotypes (i.e., linezolid-resistant, vancomycin-sensitive/daptomycin non-susceptible) were identified, as were two PVL-positive ST152 MSSA colonization isolates, previously geographically limited to Africa. CONCLUSIONS: We found a low S. aureus carriage rate with complete vancomycin susceptibility and high tetracycline resistance, which has important public health implications with regard to treatment. Additionally, the finding of PVL-positive MSSA isolates, including the expansion of a previously described limited 'divergent' clone, ST152, warrants further evaluation.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".