Training Patients for Nocturnal Home Hemodialysis
Bibliographic record
Abstract
Purpose: Nocturnal home hemodialysis (NHHD, 6–7 times weekly 6–9 h) results in better clinical outcome than conventional 3 times weekly hemodialysis. A good training program for patient and partner is a prequisite for success. We developed a training course for patients and partners. Methods: Since December 2001, we trained 20 patients and their partners to perform NHHD in 2 succeeding groups. The first group, consisting of 15 patients and their partners, started a NHHD pilot study. During this pilot study, we improved the training course. The second group of 5 were trained with this improved program. All 5 participants were home hemodialysis patients for over 1 month before starting the NHHD course. First, they learned how to handle the single needle system. Then, they performed single needle hemodialysis for 2 weeks at home. This was followed by an in‐center NHHD training, consisting of 4 conventional day‐time and 3 long (8 h) nocturnal dialysis treatments. Main targets during this training period are to learn to deal with safety precautions, online monitoring, and special machine features, and to check biochemistry and heparinization during long dialysis. 1 month after the training we evaluated the course with all participants. Results: For 9 of 15 couples in the first group, the training appeared to be exhausting. Stress factors were an overloaded program and too little experience with several new skills including needle technique before starting NHHD. The second group started the NHHD training 2 weeks after the single needle training. This second group was pleased with the training protocol. Conclusion: The training course for NHHD should not be overloaded. Patients need time to learn new skills before starting NHHD.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".