Effective interventions with chlorhexidine gluconate (CHG) to decrease hemodialysis (HD) tunneled catheter‐related infections
Bibliographic record
Abstract
Purpose: Identify practices to reduce HD catheter access related bacteremias (ARB). Methods: Data was collected per the CDC Dialysis Surveillance Network protocol. ARB was defined as a patient with a positive blood culture with no apparent source other than the vascular access catheter. ARB's were calculated in events per 100 patient months with 3 cohorts. Cohort 1 was observed for 12 months, Cohort 2 for the subsequent 10 months, and Cohort 3 for the final 10 months. Cohort 1 had weekly transparent dressing changes, cleansing of the skin and 5 minute soaking of the connection lines with 10% povidone‐iodine (PI) solution, and HCW use of clean gloves and face shield without a mask. Cohort 2 changes consisted of thrice weekly gauze dressing changes, skin cleansing with ChloraPrep, a 2% CHG/70% isopropyl alcohol applicator, masks on the patients, adding a face mask to the shield, and application of 10% PI ointment to the exit site. Cohort 3 changes included weekly application of BioPatch (BioP), an antimicrobial dressing with CHG, sterile glove use, and replacing the PI line soaks with 4% CHG. Results: The catheter‐associated ARB rate per 100 patient months was 7.9 (17ARB/216 patient months) in Cohort 1, 8.6 (13/151) in Cohort 2, and 4.7 (5/107) in Cohort 3(p = 0.31 compared with Cohorts 1 and 2 combined). During the last 2 months, in Cohort 3, 9 catheter lumen cracks occurred, with one of the patients having a bacteremia. Conclusions: Addition of CHG line soaks and BioP reduced tunneled catheter infections, although this is not statistically significant. The increased number of catheter lumen cracks raises concern with the use of CHG line soaks. Further investigation with use of CHG line soaks and the BioP for decreasing ARB is needed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".