The use of nurse‐administered vascular access audit in home hemodialysis patients: A quality initiative
Bibliographic record
Abstract
INTRODUCTION: Vascular access complications are associated with increased morbidity and mortality in home hemodialysis (HHD). Nurse-administered vascular access checklist is a feasible quality improvement strategy aimed to lower HHD access errors. METHODS: We conducted a prospective quality improvement initiative for consecutive HHD patients between April 2013 and December 2016 at the Toronto General Hospital. Vascular access audits were administered every 6 months during clinic visits and during retraining sessions after an infection. We aimed to (1) determine whether prospective serial administration of vascular audit will decrease in the number of errors performed by the patient and (2) to determine whether there is an association between the number of errors and vascular access related infection. FINDINGS: A total of 370 audits were performed on 122 patients with a mean HHD vintage of 6.7 (0.8-19.5) years. The mean number of errors per patient decreased from 1.24 ± 1.75 (baseline) to 0.33 ± 0.49 (last follow-up), P < 0.001. Among patients who had serial vascular access audits performed, there was a significant decrease in median number of errors (baseline median 1, [0-2] end of study median 0, [0-1] P = 0.01). Patients performing buttonhole cannulation made most proportion of errors as compared to CVC, 54% vs. 40% (P = 0.01) respectively; and as compared to rope ladder cannulation 54% vs. 37% (P = 0.008). We were unable to demonstrate an association between the change in patient reported errors and vascular access related infection. DISCUSSION: Vascular access audit is a feasible quality initiative, which leads to a decrease in the number of patient reported errors in vascular access. The longitudinal clinical sequelae of this strategy warrants further examination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".