Comparison of peritoneal dialysis with hemodialysis on survival of diabetic patients with end-stage kidney disease: a meta-analysis of cohort studies
Bibliographic record
Abstract
Aim: Renal replacement therapy was primary treatment for end stage kidney (ESRD) patients. Numbers of studies comparing peritoneal dialysis (PD) and hemodialysis (HD) yielded inconsistent results. The aim of this study was to assess the mortality risk between diabetic PD patients and those in HD.Methods: We included cohort studies comparing the risk of death among diabetic ESRD patients who receiving peritoneal dialysis or hemodialysis by searching Medline and Embase. Overall estimates were calculated using the random-effects model.Results: Seventeen studies were included in the meta-analyses. Mortality comparison between PD and HD in the diabetic ESRD patients showed PD significantly increased mortality rate (hazard ratio (HR) 1.20; 95% confidence interval (CI) 1.10–1.30; I2 = 89.1%). The overall HR using an intention-to-treat analysis was 1.23 with 95% CI (1.13 to 1.34). Meta-regression demonstrated PD patients from Asian country were associated with increase in mortality risk (coefficient = 0.270, SE = 0.112, p = .033).Limitation: The high heterogeneity in our meta-analyses undermined the robustness of the findings.Conclusion: ESRD patients with diabetes may benefit more from HD than PD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.036 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".