Pediatric Staphylococcus aureus Infections: Impact of Methicillin Resistance at a Canadian Center
Bibliographic record
Abstract
OBJECTIVES: Methicillin-resistant Staphylococcus aureus (MRSA) causes a wide spectrum of potentially serious infections in children. This study describes the evolving experience with S. aureus infections at a Canadian tertiary pediatric care center serving a wide geographic area. DESIGN: In this two-component study, a retrospective review of infection control databases for MRSA infection was conducted, along with a prospective component for 1 year during which all community-onset S. aureus infections were identified. Cases with methicillin resistance and susceptibility were compared. RESULTS: Review of infection control database records showed 239 unique infections, with steady increases over time. Common pulsed-field gel electrophoresis types included Canadian MRSA-7 and Canadian MRSA-10. During the 1-year prospective component, 210 clinical infections were identified, with MRSA isolated in 41%. Patients with MRSA were significantly younger than those with methicillin-susceptible isolates (4.9 vs 7.7 years, P < 0.001). The most common presentations were soft tissue infections in the emergency department, with a degree of inappropriate antimicrobial use. CONCLUSIONS: MRSA contributed to a significant proportion of S. aureus infections at a large Canadian tertiary care center. Ample opportunities exist to develop stewardship protocols, especially for the management of soft tissue infections in outpatients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".