Excellent long time survival for <scp>S</scp>wedish patients starting home‐hemodialysis with and without subsequent renal transplantations
Bibliographic record
Abstract
Survival for patients on dialysis is poor. Earlier reports have indicated that home-hemodialysis is associated with improved survival but most of the studies are old and report only short-time survival. The characteristics of patient populations are often incompletely described. In this study, we report long-term survival for patients starting home-hemodialysis as first treatment and estimate the impact on survival of age, comorbidity, decade of start of home-hemodialysis, sex, primary renal disease and subsequent renal transplantation. One hundred twenty-eight patients starting home-hemodialysis as first renal replacement therapy 1971-1998 in Lund were included. Data were collected from patient files, the Swedish Renal Registry and Swedish census. Survival analysis was made as intention-to-treat analysis (including survival after transplantation) and on-dialysis-treatment analysis with patients censored at the day of transplantation. Ten-, twenty- and thirty-year survival were 68%, 36% and 18%. Survival was significantly affected by comorbidity, age and what decade the patients started home-hemodialysis. For patients younger than 60 years and with no comorbidities, the corresponding figures were 75%, 47% and 23% and a subsequent renal transplantation did not significantly influence survival. Long-term survival for patients starting home-hemodialysis is good, and improves decade by decade. Survival is significantly affected by patient age and comorbidity, but the contribution of subsequent renal transplantation was not significant for younger patients without comorbidities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".