The use of vascular access audit and infections in home hemodialysis
Bibliographic record
Abstract
Vascular access-related infection is an important adverse event in home hemodialysis (HHD). We hypothesize that errors in self-cannulation or manipulation of dialysis vascular access are associated with increased incidence of access-related infection. We conducted a retrospective cohort study of all prevalent HHD patients at the University Health Network. All vascular access-related infections were recorded from 2006 to 2013. Errors in dialysis access were ascertained by nurse-administered vascular access checklist. Ninety-two patients had completed at least one vascular access audit. Median HHD vintage was 2.3 (0.9-5.0) years in patients with appropriate vascular access technique and 5.8 (1.5-9.4) years in patients with erroneous vascular access technique. The overall rate of infection between patients with and without appropriate vascular access technique was similar (0.27 and 0.28 infections per year, P = 0.166). Among patients who were identified with errors in dialysis access manipulation, patients with five or more errors were associated with higher rate of access-related infection (mean of 0.47 vs. 0.16 infection per patient-year, P < 0.001). The use of vascular access audit is a feasible strategy, which can identify errors in vascular access technique. Patients with a longer median HHD vintage are associated with higher risk of inappropriate vascular access technique. Patients with multiple errors in vascular access technique are associated with a higher risk of dialysis access-related infection. Prospective evaluation of the impact of vascular access audit on adverse vascular access events is warranted.
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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 | 0.018 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".