Global Trends in Rates of Peritoneal Dialysis
Bibliographic record
Abstract
Although there is a perception that the use of peritoneal dialysis is declining worldwide, compilations of global data are unavailable to test this hypothesis. We assessed longitudinal trends in the use of peritoneal dialysis from 1997 to 2008 in 130 countries. The preferred data sources were renal registries, followed by nephrology societies, health ministries, academic centers, national experts, and industry affiliates. In 2008, there were approximately 196,000 peritoneal dialysis patients worldwide, representing 11% of the dialysis population. In total, 59% were treated in developing countries and 41% in developed countries. Over 12 years, the number of peritoneal dialysis patients increased in developing countries by 24.9 patients per million population and in developed countries by 21.8 per million population. The proportion of all dialysis patients treated with peritoneal dialysis did not change in developing countries but significantly declined in developed countries by 5.3%. The use of automated peritoneal dialysis increased by 14.5% in developing countries and by 30.3% in developed countries. In summary, the number of patients treated with peritoneal dialysis rose worldwide from 1997 to 2008, with a 2.5-fold increase in the prevalence of peritoneal dialysis patients in developing countries. The proportion of all dialysis patients treated with this modality continues to decline in developed countries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".