Comparison of pathogens and their antimicrobial resistance patterns in paediatric, adult and elderly patients in Canadian hospitals
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to describe the association between age groups and antimicrobial resistance in the most commonly identified pathogens in Canadian hospitals. METHODS: Between 2007 and 2011, 27,123 clinically significant isolates, comprising 3580 isolates from children ≤ 18 years old, 12,119 isolates from adults 19-64 years old and 11,424 isolates from elderly patients aged ≥ 65 years old, were collected as part of the CANWARD surveillance study from tertiary-care centres across Canada. Antimicrobial susceptibility testing was performed according to CLSI guidelines. A multifactorial logistic regression model was used to determine the impact of demographic factors, including age groups, on antimicrobial resistance. RESULTS: Staphylococcus aureus, Escherichia coli, Streptococcus pneumoniae and Pseudomonas aeruginosa were in the top five organisms for all of the age groups. The proportions of S. aureus that were methicillin resistant, enterococci that were vancomycin resistant and E. coli that produced extended-spectrum β-lactamases were 11.2%, 0.7% and 1.0% for children, 22.8%, 4.6% and 4.3% for adults, and 28.0%, 3.8% and 4.9% for the elderly, respectively. Notable age-related differences in antimicrobial resistance patterns included the following: significantly less methicillin, clindamycin, clarithromycin and trimethoprim/sulfamethoxazole resistance in S. aureus from children; for E. coli, higher cefazolin and ciprofloxacin resistance in the elderly and less ceftriaxone, ciprofloxacin and gentamicin resistance in isolates from children; more S. pneumoniae isolates with penicillin MICs >1 mg/L in children; and for P. aeruginosa, higher resistance rates for meropenem, ciprofloxacin and levofloxacin in adults. CONCLUSIONS: The assessment of antimicrobial susceptibility patterns by age group revealed that resistance rates are often higher in the older age groups; however, considerable variability in age-specific resistance trends for different pathogen-antimicrobial combinations was noted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".