Healthcare-associated infections and antimicrobial resistance in Canadian acute care hospitals, 2016–2020
Bibliographic record
Abstract
Background: Canadians experience increased morbidity, mortality and healthcare costs due to healthcare-associated infections (HAIs) and antimicrobial resistance (AMR). The Canadian Nosocomial Infection Surveillance Program (CNISP) collects and utilizes epidemiologic and laboratory surveillance data to inform infection prevention and control and antimicrobial stewardship programs and policies. The objective of this report is to describe the epidemiologic and laboratory characteristics and trends of HAIs and AMR from 2016 to 2020 using surveillance data provided by Canadian hospitals participating in the CNISP. Methods: (CPE). Case counts, rates, outcome data, molecular characterization and antimicrobial resistance profiles are presented. Results: =0.050). Conclusion: Surveillance findings from a national network of Canadian acute care hospitals indicate that rates of MRSA and VRE bloodstream infections, CPE infections and colonizations have increased substantially between 2016 and 2020 while rates of CDI have decreased. The collection of detailed, standardized surveillance data and the consistent application of infection prevention and control practices in acute care hospitals are critical in reducing the burden of HAIs and AMR infections in Canada. Further investigations into the impact of coronavirus disease 2019 and associated public health measures are underway.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".