Prevalence of antimicrobial-resistant organisms in smaller Canadian hospitals: Community, Rural, and Northern Acute Care Point Prevalence (CNAPP-19) Survey, 2019
Bibliographic record
Abstract
Background:The availability of national data on the prevalence of antimicrobial resistant infections in smaller, community, northern and rural acute care hospitals is limited.The objective of this article is to determine the prevalence of infections caused by selected antimicrobialresistant organisms (AROs) in these smaller hospitals.Methods: A point prevalence survey was conducted by 55 hospitals between February and May 2019 and included representation from all 10 Canadian provinces.Eligible hospitals were those with 350 or fewer beds.Data were collected on hospital characteristics.De-identified patient data were collected on selected infections (pneumonia, urinary tract infections, bloodstream infections, skin/soft tissue infections, surgical site infections, and Clostridioides difficile infections) for selected AROs (methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococci, extended-spectrum β-lactamase-producing organisms and carbapenemase-producing organisms).Data on antimicrobial prescribing and infection prevention and control precautions were also collected.Results: A total of 3,640 patients were included in the survey.Median patient age was 73 years, and 52.8% (n=1,925) were female.Selected infections were reported in 14.4% (n=524) of patients, of which 6.9% (n=36) were associated with an ARO infection.Infection prevention and control additional precautions were in place for 13.7% (n=500) of patients, of which half (51.0%, n=255) were due to an ARO.Approximately one third (35.2%, n=1,281) of patients had at least one antimicrobial prescribed.Conclusion: Antimicrobial-resistant organisms remain a serious threat to public health in Canada.The results of this survey warrant further investigation into AROs in smaller Canadian hospitals as a potential reservoir of antimicrobial resistance.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".