Patient Training and Patient Safety in Home Hemodialysis
Bibliographic record
Abstract
The success of a home hemodialysis program depends largely on a patient safety framework and the risk tolerance of a home dialysis program. Dialysis treatments require operators to perform dozens of steps repeatedly and reliably in a complex procedure. For home hemodialysis, those operators are patients themselves or their care partners, so attention to safety and risk mitigation is front of mind. While newer, smaller, and more user-friendly dialysis machines designed explicitly for home use are slowly entering the marketplace, teaching patients to perform their own treatments in an unsupervised setting hundreds of times remains a foundational programmatic obligation regardless of machine. Just how safe is home hemodialysis? How does patient training affect this safety? There is a surprising lack of literature surrounding these questions. No consensus exists among home hemodialysis programs regarding optimized training schedules or methods, with each program adopting its own approach on the basis of local experience. Furthermore, there are little available data on the safety of home hemodialysis as compared with conventional in-center hemodialysis. This review will outline considerations for training patients on home hemodialysis, discuss the safety of home hemodialysis with an emphasis on the risk of serious and life-threatening adverse effects, and address the methods by which adverse events are monitored and prevented.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".