SP446THE PRESCRIPTION IN PERITONEAL DIALYSIS: INTERNATIONAL COMPARISON FROM THE PERITONEAL DIALYSIS OUTCOMES AND PRACTICE PATTERNS STUDY (PDOPPS)
Bibliographic record
Abstract
Introduction and Aims: Large variability in peritoneal dialysis (PD) use and PD outcomes (e.g. technique survival) exists across countries. Along with differences in patients’ case mix, variation in PD practices is likely to contribute; however, little is known on this regional variation. Our study reports PD prescription among participants in PDOPPS, which is the largest international study of PD practices to date. Methods: The PDOPPS is a prospective cohort study of PD patients ongoing in Australia, Canada, Japan, the United Kingdom (UK), and the United States (US) in collaboration with the International Society of Peritoneal Dialysis (ISPD). Here we present preliminary baseline data on PD prescription among PDOPPS participants on manual (CAPD) and automated PD (APD) (n=1,306) by country. Data were not yet available in the UK. Results: Patient demographics and PD prescription in each country are shown (Table). CAPD was most common in Japan, while APD was predominant elsewhere. Among CAPD patients those in US were less likely to have 3 or fewer exchanges. In Australia icodextrin was always used for long exchanges, and less so elsewhere. In Japan, number of APD cycles, total prescribed volume and dwell volumes (both in CAPD and APD) were lower than in other countries. Low Kt/V was less common in the US and Australia, where 24 hours urine volume was highest.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".