Incremental peritoneal dialysis in incident end-stage kidney disease patients
Bibliographic record
Abstract
BACKGROUND: This retrospective cohort study investigated the characteristics and outcomes of the end-stage kidney disease (ESKD) patients treated with incremental peritoneal dialysis (PD) at a large academic centre. METHODS: ESKD patients initiating PD with a dialysate volume ≤6 L/day were analysed. RESULTS: . The unadjusted 1- to 5-year patient survival rate was 89.6%, 80.4%, 65.4%, 62.7% and 48.8%, respectively, and the corresponding time on PD therapy rate was 95.1%, 89.1%, 89.1%, 82.4% and 77.6%. Greater initial PD dose (hazard ratio = 1.608, 95% confidence interval 1.089-2.375) was associated with death after adjusting for age, Charlson comorbidity index (CCI), haemodialysis prior to PD, assisted PD and baseline RKF, likely as a result of residual confounding. There was no association with PD discontinuation. The average peritonitis rate and hospitalisation rate were 0.122 and 0.645 episodes per patient-year, respectively. The dialysate volume increased from 4.5 (4.3-5.7) L/day to 8.0 (6.0-9.8) L/day at 5 years. Fifty-seven (32.6%) patients graduated to full-dose PD at a median time of 10.3 (6.2, 15.7) months. Male sex, greater body mass index and lower baseline serum albumin were risk factors for increasing PD dose to over 6 L/day within 1 year. CONCLUSIONS: Incremental PD is a safe approach to initiate dialysis, and it offers satisfactory outcomes. Close monitoring, comprehensive evaluation of clinical responses and prompt adjustment of the prescription as needed play a crucial role in this patient-centred treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".