Trends in antibiotic resistance over time among pathogens from Canadian hospitals: results of the CANWARD study 2007-11
Bibliographic record
Abstract
OBJECTIVES: Antimicrobial resistance patterns change over time and longitudinal surveillance studies provide insight into these trends. We sought to describe the important trends in antimicrobial resistance in key pathogens across Canada to provide useful information to clinicians, policy makers and industry, to assist in optimizing antimicrobial therapy, formulary choices and drug development. METHODS: We analysed longitudinal data from the CANWARD study using a multivariate regression model to control for possible effects of patient demographics on resistance, in order to assess the impact of time on antimicrobial resistance independent of other measured variables. RESULTS: We identified several key trends in common pathogens. In particular, we observed a statistically significant increase in the proportion of Escherichia coli isolates that were resistant to extended-spectrum cephalosporins and fluoroquinolones, an increase in the proportion of Klebsiella pneumoniae isolates that were resistant to extended-spectrum cephalosporins, a reduction in the proportion of Staphylococcus aureus that were methicillin, clindamycin and trimethoprim/sulfamethoxazole resistant, and a reduction in the proportion of Pseudomonas aeruginosa that were fluoroquinolone and gentamicin resistant. CONCLUSIONS: Although some of these trends, such as the dramatic increase in fluoroquinolone and cephalosporin resistance in E. coli, can be attributed to the emergence and global spread of resistant clones (e.g. ST131 E. coli), others remain unexplained. However, recognizing these trends remains important to guide changes in empirical antimicrobial therapy and drug development.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| 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".