Stage V Chronic Kidney Disease: A plea for Home Dialysis
Bibliographic record
Abstract
Summary Overall, home dialysis remains underutilized in much of the world. It is only in some parts of the world, such as Hong Kong, Mexico, Thailand, the Netherlands, Finland, Denmark, Iceland, Australia, Canada and New Zealand, that home dialysis is used for more than 20% of the dialysis population. It is observed that in most high- and middle-income countries, home dialysis is generally more economical than center-based dialysis. According to the results of the REIN 2020 registry, the share of out-of-center dialysis in France has been increasing since 2012. This is mainly due to the increase in the number of patients treated in low medicalized dialysis units (LMDUs) (+7.3% per year between 2012 and 2016, then +3.8% between 2016 and 2020). In contrast, the proportion of patients treated at home has changed little. The percentage of patients on peritoneal dialysis is decreasing (-1.8% per year). Home hemodiaysis is increasing (+10.3% per year) but remains very marginal (1% of dialysis patients). Finally, the advanced age of dialysis patients, which is constantly increasing, cannot be ignored. The proportion of these very old patients has increased from 10.5% in 2012 to 12.5% in 2020. However, during the COVID-19 epidemic, several articles in the literature have demonstrated the protective effect of home dialysis in all its forms (peritoneal dialysis and home hemodialysis) against SARS-CoV-2 infection. We report on the status of home dialysis in France, its advantages and proposals for its development, as presented at the Home Dialysis Day (DIADOM) of University Seminars of Nephrology (SUN) in Paris in January 2022.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".