The ERA Registry Annual Report 2019: summary and age comparisons
Bibliographic record
Abstract
BACKGROUND: Data on renal replacement therapy (RRT) for end-stage renal disease were collected by the European Renal Association (ERA) Registry via national and regional renal registries in Europe and countries bordering the Mediterranean Sea. This article provides a summary of the 2019 ERA Registry Annual Report, including data from 34 countries and additional age comparisons. METHODS: Individual patient data for 2019 were provided by 35 registries and aggregated data by 17 registries. Using these data, the incidence and prevalence of RRT, the kidney transplantation activity and the survival probabilities were calculated. RESULTS: In 2019, a general population of 680.8 million people was covered by the ERA Registry. Overall, the incidence of RRT was 132 per million population (p.m.p.). Of these patients, 62% were men, 54% were ≥65 years of age and 21% had diabetes mellitus as primary renal disease (PRD), and 84% had haemodialysis (HD), 11% had peritoneal dialysis (PD) and 5% had pre-emptive kidney transplantation as an initial treatment modality. The overall prevalence of RRT on 31 December 2019 was 893 p.m.p., with 58% of patients on HD, 5% on PD and 37% living with a kidney transplant. The overall kidney transplant rate was 35 p.m.p. and 29% of the kidney grafts were from a living donor. The unadjusted 5-year survival probability was 42.3% for patients commencing dialysis, 86.6% for recipients of deceased donor grafts and 94.4% for recipients of living donor grafts in the period 2010-14. When comparing age categories, there were substantial differences in the distribution of PRD, treatment modality and kidney donor type, and in the survival probabilities.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".