Changes in fluoroquinolone resistance over 5 years (CANWARD 2007-11) in bacterial pathogens isolated in Canadian hospitals
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to analyse Canadian national surveillance data, specifically fluoroquinolone resistance, from 2007 to 2011 inclusive, to determine trends in resistance over time and to assess correlations with patient demographic factors. METHODS: All isolates of Escherichia coli, Klebsiella pneumoniae, Enterobacter cloacae, Pseudomonas aeruginosa, Staphylococcus aureus and Streptococcus pneumoniae collected by the 10 sites that participated in the annual CANWARD surveillance studies in each of the 5 years were included in this analysis. A multifactorial logistic regression model was used to determine the variables with significant impact on fluoroquinolone resistance. RESULTS: The proportion of E. coli isolates resistant to ciprofloxacin increased significantly (P = 0.0005) between 2007 (20.0%) and 2011 (29.2%), although similar increases were not seen in K. pneumoniae, E. cloacae, P. aeruginosa and S. pneumoniae (tested against levofloxacin) (P > 0.05). Among isolates of S. aureus, there was a significant decrease in ciprofloxacin resistance from 34.4% in 2007 to 24.6% in 2011 (P < 0.0001). Resistance to ciprofloxacin was a component of most multidrug-resistant (MDR) phenotypes for E. coli, K. pneumoniae, E. cloacae, P. aeruginosa and S. aureus. CONCLUSIONS: A significant increase in the percentage of ciprofloxacin-resistant E. coli, primarily among urine isolates, and a significant decrease in the percentage of ciprofloxacin-resistant S. aureus occurred between 2007 and 2011. Notably, MDR isolates were frequently fluoroquinolone resistant for all organisms studied, except S. pneumoniae.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".