Effects of initial peritoneal dialysis prescription on clinical outcomes in Japanese peritoneal dialysis patients: a cohort study
Bibliographic record
Abstract
Effects of the initial peritoneal dialysis (PD) prescription on clinical outcomes are unknown in Japan. We conducted a cohort study using data from Peritoneal Dialysis Outcomes and Practice Patterns Study. The patients were divided into two groups by the volume of the initial PD prescription (≤ 4 L/day or > 4 L/day). Cause-specific Cox proportional hazards survival models were used to model the association between different PD prescriptions and the clinical outcomes. The outcomes included transfer to HD, mortality, the composite of mortality and transfer to HD, peritonitis, hospitalization, and the patient-reported outcomes (PROs). Of the 342 patients, 98 were prescribed ≤ 4 L/day, and 244 were prescribed > 4 L/day. Patients prescribed ≤ 4 L/day were older with a lower percentage being male, had more cardiovascular and cerebrovascular disease but lower diabetes prevalence, were more likely to be receiving CAPD, used more assisted PD, and had lower BMI and mean serum creatinine levels. There were no significant differences between groups in terms of transfer to HD, mortality, transfer to HD or mortality, hospitalization, incidence of peritonitis, and PROs. Patients with initial PD prescriptions of ≤ 4 L/day compared to > 4 L/day had similar clinical outcomes. This practice may provide health economic benefits in Japan.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".