The effect of timing of the first kidney transplantation on survival in children initiating renal replacement therapy
Bibliographic record
Abstract
BACKGROUND: Controversy exists concerning the timing of the first kidney transplantation for children who need to start renal replacement therapy (RRT). Our aim was to estimate the effect of timing of the first transplantation on patient survival in children, for the first time also taking into account the mortality on dialysis before transplantation. METHODS: We included 2091 patients who started RRT between the age of 3 and 18 years in the period 1988-2007, from 13 European renal registries. A multistate model was used to simulate patient survival assuming (i) pre-emptive transplantation, (ii) transplantation after 1 or 2 years on dialysis and (iii) remaining on dialysis. RESULTS: Over the 20-year period, the highest 8-year survival probabilities were achieved in children transplanted pre-emptively {living donor (LD): 95.9% [95% confidence interval (CI): 93.1-98.8], deceased donor (DD): 95.3% (95% CI: 90.9-99.9)} rather than after 2 years of dialysis [LD: 94.2% (95% CI: 91.6-96.8), DD: 93.4% (95% CI: 91.0-95.9)], although these differences were not statistically significant. CONCLUSIONS: Even after taking mortality on dialysis into account, the potentially negative effect of postponing transplantation for 1 or 2 years was relatively small and not statistically significant. Therefore, if pre-emptive transplantation is not possible, starting RRT with a short period of dialysis and receiving a transplant thereafter seems an acceptable alternative from the perspective of patient survival.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".