Canadian Patterns of Antimicrobial Resistance: Overview of Current Trends Related to Hospital Pathogens
Bibliographic record
Abstract
Selection of appropriate empirical antibiotic therapy is dependent on many factors, not the least of which is an understanding of antimicrobial resistance rates in the patient population that is undergoing treatment. Resistance rates may vary by geographical location, institution, hospital ward or unit, and even body site of infection. The present paper reviews the currently available Canadian data regarding resistance rates for some of the most common hospital‐acquired pathogens, including Escherichia coli, Klebsiella species, Pseudomonas aeruginosa, Enterobacter species, Staphylococcus aureus and Enterococcus species. Current data suggest that the rates of extended‐spectrum beta‐lactamases among E coli and Klebsiella species remain relatively low across Canada. There are very little data on the epidemiology of ampC cephalosporinases among Gram‐negative organisms, although combined American and Canadian data suggest that ceftazidime resistance rates for Enterobacter species range from 17.1% to 24.8%. The increased use of fluoroquinolones has led to an increase in resistance rates among most Gram‐negative organisms. In the late 1990s, ciprofloxacin resistance rates for P aeruginosa were reported to be approximately 20%. For the Gram‐positive organisms, methicillin resistance rates for S aureus have been reported to be as high as 10% and vancomycin resistance rates for Enterococcus species have been reported to be less than 1%. Additional data that are representative of all regions of Canada are needed. Continued surveillance, antibiotic stewardship, and adherence to good infection prevention and control measures will lead to a better understanding of the epidemiology of antimicrobial resistance in Canadian hospitals, as well as help to control its spread.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.026 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".