Factors affecting uptake of home hemodialysis among self‐care dialysis unit patients
Bibliographic record
Abstract
INTRODUCTION: Superior outcomes have been reported among hemodialysis (HD) patients who take active control over their dialysis treatment either at self-care satellite dialysis units or home compared to the regular in-center hemodialysis patient. Although the differences between the home hemodialysis (HHD) and self-care in-center HD (SCHD) are not well described, the growing literature on the superior outcomes of HHD suggests that HHD is the better option. METHODS: We performed a cross-sectional study in a stand-alone self-care unit to examine the differences in patients that are keen to consider HHD and those who are not. FINDINGS: A cross-sectional sample of 44 patients completed a structured interview and the distress thermometer score used to assess psychological stress. Only 68% of patients reported to have heard about the benefits of HHD despite the long-established history and availability of the modality in the unit. One of the more critical findings in our study was that the cohort of patients who were keen to consider HHD believed that self-care and HHD would improve their quality of life (P < 0.05). Specifically, the perceived benefits stated by those willing to consider HHD were the lack of need to travel, association with better outcomes and the possibility of having the treatment in the comfort of home (P < 0.05). DISCUSSION: We surmise that the answers expressed in this survey likely reflect a difference in perceptions of self-care and beliefs about HHD; hence, the importance of introducing HHD education earlier in the course of their chronic kidney disease journey.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".