Why and how should we promote home dialysis for patients with end-stage kidney disease during and after the coronavirus 2019 disease pandemic? A French perspective
Bibliographic record
Abstract
The health crisis induced by the pandemic of coronavirus 2019 disease (COVID-19) has had a major impact on dialysis patients in France. The incidence of infection with acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the first wave of the COVID-19 epidemic was 3.3% among dialysis patients-13 times higher than in the general population. The corresponding mortality rate was high, reaching 21%. As of 19th April, 2021, the cumulative prevalence of SARS-CoV-2 infection in French dialysis patients was 14%. Convergent scientific data from France, Italy, the United Kingdom and Canada show that home dialysis reduces the risk of SARS-CoV-2 infection by a factor of at least two. Unfortunately, home dialysis in France is not sufficiently developed: the proportion of dialysis patients being treated at home is only 7%. The obstacles to the provision of home care for patients with end-stage kidney disease in France include (i) an unfavourable pricing policy for home haemodialysis and nurse visits for assisted peritoneal dialysis (PD), (ii) insufficient training in home dialysis for nephrologists, (iii) the small number of administrative authorizations for home dialysis programs, and (iv) a lack of structured, objective information on renal replacement therapies for patients with advanced chronic kidney disease (CKD). We propose a number of pragmatic initiatives that could be simultaneously enacted to improve the situation in three areas: (i) the provision of objective information on renal replacement therapies for patients with advanced CKD, (ii) wider authorization of home dialysis networks and (iii) price increases in favour of home dialysis procedures.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.019 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".