Effective prevention bundle to eliminate catheter-related bloodstream infections in ambulatory hemodialysis patients
Bibliographic record
Abstract
Background: Hamad General Hospital (HGH) is the principal provider of dialysis in the state of Qatar, comprising a total of four facilities in different cities. Infection rates in dialysis patients are increasingly used as a surrogate marker for measuring patient safety and quality of healthcare. These infections are associated with substantial morbidity, mortality, and excess healthcare costs. We observed an elevated rate of hemodialysis catheter-related bloodstream infections (HD-CRBSI) in our outpatient dialysis facilities (1.4/1,000 Central Venous Catheter [CVC] days) in 2011. Our goal was to reduce our HD-CRBSI rate by 80% within a period of four years in HGH ambulatory dialysis facilities. Methods: HD-CRBSIs are defined as the presence of positive blood cultures in a febrile catheter-dependent patient in the absence of alternative sources of infection upon clinical evaluation. The project was led by the HGH quality improvement program director in coordination with a multidisciplinary team (nephrologists, nurses, vascular coordinators, a patient educator, and an infection control team) after implementation of a bundle of infection prevention measures. Results: The rate of HD-CRBSI was reduced from 1.4/1,000 CVC days in 2011 to 0.014 in 2017, achieving a 99% reduction rate (p < 0.001). Conclusions: Strict implementation of our new infection prevention measures bundle is sufficient to significantly reduce HD-CRBSIs.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".