The Canadian Nosocomial Infection Surveillance Program: Keeping an eye on antimicrobial resistance in Canadian hospitals since 1995
Bibliographic record
Abstract
Surveillance is essential to inform evidence-based policy and control measures that combat antimicrobial resistance (AMR).The Canadian Nosocomial Infection Surveillance Program (CNISP) collaborates with 88 sentinel hospitals across Canada to conduct prospective surveillance of infections and antimicrobial resistant organisms important to hospital infection prevention and control.This article aims to increase awareness of CNISP hospital-based surveillance activities.Since its inception in 1995, the scope of CNISP has expanded to include community-associated infections, outpatient Clostridioides difficile infections, viral respiratory infections such as coronavirus disease 2019, and emerging pathogens such as Candida auris.This change in scope, along with expansion to include rural, northern and community hospitals, has improved the generalizability of CNISP surveillance data.To generate actionable surveillance data, CNISP integrates demographic and clinical data abstracted from patient charts with molecular and microbiological data abstracted from laboratory testing.These data serve as a benchmark for participating hospitals and stakeholders to assess the burden of AMR in hospital and intervene as needed.Further, CNISP surveillance data are now available on a public-facing data blog that provides interactive visualizations and data syntheses sooner than peer-reviewed publications.Future directions of CNISP include the Simplified Dataset, which will capture aggregate AMR data from hospitals outside of the CNISP network, surveillance in long-term care facilities and a fourth point prevalence survey.Given its strengths and future directions, CNISP is well positioned to serve as the reference point for hospital-based AMR data in Canada.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".