Survey of home hemodialysis patients and nursing staff regarding vascular access use and care
Bibliographic record
Abstract
Vascular access infections are of concern to hemodialysis patients and nurses. Best demonstrated practices (BDPs) have not been developed for home hemodialysis (HHD) access use, but there have been generally accepted practices (GAPs) endorsed by dialysis professionals. We developed a survey to gather information about training provided and actual practices of HHD patients using the NxStage System One HHD machine. We used GAP to assess training used by nurses to teach HHD access care and then assess actual practice (adherence) by HHD patients. We also assessed training and adherence where GAPs do not exist. We received a 43% response rate from patients and 76% response from nurses representing 19 randomly selected HHD training centers. We found that nurses were not uniformly instructing HHD patients according to GAP, patients were not performing access cannulation according to GAP, nor were they adherent to their training procedures. Identification of signs and symptoms of infection was commonly trained appropriately, but we observed a reluctance to report some signs and symptoms of infection by patients. Of particular concern, when aggregating all steps surveyed, not a single nurse or patient reported training or performing all steps in accordance with GAP. We also identified practices for which there are no GAPs that require further study and may or may not impact outcomes such as infection. Further research is needed to develop strategies to implement and expand GAP, measure outcomes, and ultimately develop BDP for HHD to improve infectious complications.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".