Assessing the magnitude and trends in hospital acquired infections in Canadian hospitals through sequential point prevalence surveys
Bibliographic record
Abstract
BACKGROUND: Healthcare acquired infections (HAI) are an important public health problem in developed countries, but comprehensive data on trends over time are lacking. Prevalence surveys have been used as a surrogate for incidence studies and can be readily repeated. METHODS: The Canadian Nosocomial Infection Surveillance Program conducted prevalence surveys in 2002 and 2009 in a large network of major Canadian acute care hospitals. NHSN definitions of HAI were used. Use of isolation precautions on the survey day was documented. RESULTS: In 2009, 9,953 acute care inpatients were surveyed; 1,234 infections (124/1000) were found, compared to 111/1000 in 2002, (p < 0.0001). There was increased prevalence of urinary tract infection (UTI) and Clostridium difficile, offset by decreases in pneumonia and bloodstream infection. Use of isolation precautions increased from 77 to 148 per 1000 patients (p < 0.0001), attributable to increased use of contact precautions in patients infected or colonized with antimicrobial resistant organisms. CONCLUSION: Between 2002 and 2009 HAI prevalence increased by 11.7 % in a network of major Canadian hospitals due to increases in Clostridium difficile and urinary tract infection. The use of isolation precautions increased by 92.2 % attributable to increased contact isolation. National prevalence surveys are useful tools to assess evolving trends in HAI.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".