A survey of Preventive Measures Used and their Impact on Central Line-Associated Bloodstream Infections (CLABSI) in Intensive Care Units (SPIN-BACC)
Bibliographic record
Abstract
BACKGROUND: The Quebec central line-associated bloodstream infections (CLABSI) in intensive care units (ICUs) Surveillance Program saw a decrease in CLABSI rates in most ICUs. Given the surveillance trends observed in recent years, we aimed to determine what preventive measures have been implemented, if compliance to measures was monitored and its impact on CLABSI incidence rates. METHODS: All hospitals participating in the Quebec healthcare-associated infections surveillance program (SPIN-BACC - n = 48) received a 77-question survey about preventive measures implemented and monitored in their ICU. The questionnaire was validated for construct, content, face validity, and reliability. We used Poisson regression to measure the association between compliance monitoring to preventive measures and CLABSI rates. RESULTS: Forty-two (88%) eligible hospitals completed the survey. Two components from the maximum barrier precautions were used less optimally: cap (88%) and full sterile body drape (71%). Preventive measures reported included daily review of catheter need (79%) and evaluation of insertion site for the presence of inflammation (90%). Two hospitals rewired lines even if an infection was suspected or documented.In adult ICUs, there was a statistically significant greater decrease in CLABSI rates in ICUs that monitored compliance to preventive insertion measures, after adjusting for teaching status and the number of hospital beds (p = 0.036). CONCLUSIONS: Hospitals participating to the SPIN-BACC program follow recommendations for CLABSI prevention, but only a minority locally monitor their application. Compliance monitoring of preventive measures for catheter insertion was associated with a decrease in CLABSI incidence rates.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".