Device‐associated health care‐associated infections: The effectiveness of a 3‐year prevention and control program in the Republic of Cyprus
Bibliographic record
Abstract
BACKGROUND: Device-associated health care-associated infections (DA-HAIs) are a major threat to patient safety, particularly in the Intensive Care Unit (ICU). This study aimed to evaluate the effectiveness of a bundle of infection control measures to reduce DA-HAIs in the ICU of a General Hospital in the Republic of Cyprus, over a 3-year period. METHODS: We studied 599 ICU patients with a length of stay (LOS) for at least 48 hours. Our prospective cohort study was divided into three surveillance phases. Ventilator-associated pneumonia (VAP), central line-associated blood-stream infections (CLABSI), and catheter-associated blood-stream infections (CAUTI) incidence rates, LOS, and mortality were calculated before, during, and after the infection prevention and control programme. RESULTS: There was a statistically significant reduction in the number of DA-HAI events during the surveillance periods, associated with DA-HAIs prevention efforts. In 2015 (prior to programme implementation), the baseline DA-HAIs instances were 43: 16 VAP (10.1/1000 Device Days), 21 (15.9/1000DD) CLABSIs, and 6 (2.66/1000DD) CAUTIs, (n = 198). During the second phase (2016), CLABSIs prevention measures were implemented and the number of infections were 24: 14 VAP (12.21/1000DD), 4 (4.2/1000DD) CLABSIs, and 6 (3.22/1000DD) CAUTIs, (n = 184). During the third phase (2017), VAP and CAUTI prevention measures were again implemented and the rates were 6: (3 VAP: 12.21/1000DD), 2 (1.95/1000DD) CLABSIs, and 1 (0.41/1000DD) CAUTIs, (n = 217). There was an overall reduction of 87% in the total number of DA-HAIs instances for the period 1 January 2015 to 31 December 2017. CONCLUSIONS: The significant overall reduction in DA-HAI rates indicates that a comprehensive infection control programme can affect DA-HAI rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".