Community‐Associated Methicillin‐Resistant <i>Staphylococcus aureus</i> in a Pediatric Emergency Department in Newfoundland and Labrador
Bibliographic record
Abstract
BACKGROUND: First-generation cephalosporins and antistaphylococcal penicillins are typically the first choice for treating skin and soft tissue infections (SSTI), but are not effective for infections caused by methicillin-resistant Staphylococcus aureus (MRSA). It is currently unclear what percentage of SSTIs is caused by community-associated MRSA in different regions in Canada. OBJECTIVES: To determine the incidence of MRSA in children presenting to a pediatric emergency department with SSTI, and to determine which antibiotics were used to treat these infections. METHODS: All visits to a pediatric emergency department were reviewed from April 15, 2010 to April 14, 2011. Diagnoses of cellulitis, abscess, impetigo, folliculitis and skin infection (not otherwise specified) were reviewed in detail to determine whether a culture was taken and which antibiotic was prescribed. RESULTS: There were 367 cases of SSTI diagnosed over the study period. Forty-five (12.3%) patients had lesions that were swabbed for culture and sensitivity. S aureus was the most common organism found, with 14 (66%) methicillin-sensitive cases and seven (33%) methicillin-resistant cases. Of the seven cases of MRSA identified, only one patient had clear risk factors for hospital-acquired MRSA. First-generation cephalosporins were initially prescribed for 280 (76%) patients. CONCLUSIONS: The overall incidence of MRSA in the population presenting to a pediatric emergency department in Newfoundland and Labrador appeared to be low, although only a small percentage of infections were cultured. At this time, there appears to be no need to change empirical antibiotic coverage, which remains a first-generation cephalosporin.
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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".