Time trend in access to the waiting list and renal transplantation: a comparison of four European countries
Bibliographic record
Abstract
BACKGROUND: To examine the time trend and international differences in access to the waiting list and renal transplantation of patients with end-stage kidney disease. METHODS: We included all patients (n = 30 961) from Austria, Norway, the Netherlands and Scotland who started renal replacement therapy (RRT) between 1995 and 2003 with their kidney transplant waiting list data (until 31 December 2005) and follow-up data on RRT and mortality (until 31 December 2007). The outcome measure was access to the waiting list within 2 years and to a first renal transplant within 4 years from the start of RRT, expressed as incidence per million age-related population (p.m.a.r.p.) per year. To estimate trends over time, mean percentage annual change (MPAC) and 95% confidence interval (CI) were calculated. RESULTS: In each country, the number of patients starting RRT > 65 years increased significantly over time, whereas the number of renal transplants did not increase to the same extent. Only in Norway were almost all patients on the waiting list transplanted within 4 years of RRT start if they were < 65 years. In patients who started RRT > 65 years, the access to renal transplantation was high in Norway (49 p.m.a.r.p.) and low in Austria ( < 26 p.m.a.r.p.), the Netherlands and Scotland (both < 10 p.m.a.r.p.) but increased significantly in Austria (MPAC = 9.8%; 95% CI = 3.9-16.9) and the Netherlands (MPAC = 9.0%; 95% CI = 3.2-15.0). CONCLUSION: Only in Norway, virtually all patients on the waiting list < 65 years received a transplant within 4 years after the start of RRT and, remarkably, also most of those > 65 years of age.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".