Antimicrobial resistance surveillance in Canadian hospitals, 2007−2012
Bibliographic record
Abstract
Background: The Canadian Nosocomial Infection Surveillance Program (CNISP) is a collaborative effort of the Public Health Agency of Canada's Centre for Communicable Diseases and Infection Control, the National Microbiology Laboratory, and 54 largely university-affiliated tertiary care sentinel hospitals in 10 provinces across Canada.Objective: To provide a summary of antibiotic resistance rates of four key antibiotic resistant organisms in major hospitals across Canada from January 1, 2007, to December 31, 2012.Methods: Patients' clinical and demographic data and associated results of laboratory analyses were submitted to the Agency by participating hospitals.The infection rates were summarized per 1,000 patient admissions at national and regional levels.Results: In Canada, the overall health care-associated Clostridium difficile infection (HA-CDI), HA-CDI rates peaked in 2008 at 5.8 HA-CDI infections per 1,000 patient admissions then remained stable between 2009 and 2012 at approximately 5 HA-CDI infections per 1,000 admissions; the West and Central regions had higher rates than the Eastern region.The rates of methicillin-resistant Staphylococcus aureus (MRSA) peaked in 2009 at 9.5 MRSA infections per 1,000 patient admissions then decreased to 8.8 MRSA infections per 1,000 admissions in 2012, with the Central region having higher rates than the Western and Eastern regions.The rates of vancomycin-resistant Enterococci (VRE), have been low but rising with 0.08 VRE infections per 1,000 patient admissions in 2007, gradually rising to 0.5 VRE infections per 1,000 admissions in 2012, with consistently higher rates in the Western region, slightly lower rates in the Central region and the lowest rates in the Eastern region.The rates of carbapenem-resistant Enterobacteriaceae (CRE) have been measured since 2010 and have been low and stable, with 0.11 CRE infections per 1,000 patient admissions in 2010 and 0.14 CRE infections per 1,000 admissions in 2012, with higher rates in the Western and Central regions and lower rates in the Eastern region. Conclusion:In Canada, of the four antibiotic resistant organisms under surveillance, HA-CDI and MRSA have been gradually decreasing, VRE is low but rising, and CRE remains low with Western and Central rates consistently higher than Eastern rates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".