Understanding barriers to home‐based and self‐care in‐center hemodialysis
Bibliographic record
Abstract
Despite superior outcomes and lower associated costs, relatively few patients with end-stage renal disease undergo self-care or home hemodialysis. Few studies have examined patient- and physician-specific barriers to self-care and home hemodialysis in the modern era. The degree to which innovative technology might facilitate the adoption of these modalities is unknown. We surveyed 250 patients receiving in-center hemodialysis and 51 board-certified nephrologists to identify key barriers to adoption of self-care and home hemodialysis. Overall, 172 (69%) patients reported that they were "likely" or "very likely" to consider self-care hemodialysis if they were properly trained on a new hemodialysis system designed for self-care or home use. Nephrologists believed that patients were capable of performing many dialysis-relevant tasks, including: weighing themselves (98%), wiping down the chair and machine (84%), clearing alarms during treatment (53%), taking vital signs (46%), and cannulating vascular access (41%), but thought that patients would be willing to do the same in only 69%, 34%, 31%, 29%, and 16%, respectively. Reasons that nephrologists believe patients are hesitant to pursue self-care or home hemodialysis do not correspond in parallel or by priority to reasons reported by patients. Self-care and home hemodialysis offer several advantages to patients and dialysis providers. Overcoming real and perceived barriers with new technology, education and coordinated care will be required for these modalities to gain traction in the coming years.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".