Home hemodialysis: A successful option for obese and bariatric people with end‐stage kidney disease
Bibliographic record
Abstract
The increasing prevalence of obesity in developed countries is reflected in the chronic kidney disease, dialysis, and transplant populations. The added risk factor of obesity increases the risk of vascular events, inflammation, insulin resistance, blood pressure, dyslipidemia, and mortality risk. Nephrology center policies may exclude obese people from transplantation programs resulting in many years of dialysis. The case of a 215-kg Australian male who has successfully dialyzed at home for more than 8 years will be used to illustrate the important considerations and clinical support that these people require for successful home dialysis treatment. The aim of this paper is to report on a program that has successfully trained 23 obese (body mass index >30) people who commenced on home hemodialysis between 2001 and 2009. Body weight ranged between 94.0 and 215 kg (mean 126, SD 26.19) and body mass index ranged between 34.9 and 71 (mean 43.38, SD 9.99) at the start of home training. During the 8.5 years of follow-up, average time on home dialysis was 43.7 months. Home hemodialysis is a feasible treatment for obese people to facilitate longer and more frequent dialysis, resulting in improved hemodynamic stability and improved quality of life. For obese people with end-stage kidney disease, home hemodialysis has shown to be cost-effective and can result in greater treatment efficacy than in-center hospital dialysis.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".