Incidence and risk factors of early surgical complications in young renal transplant recipients: A persistent challenge
Bibliographic record
Abstract
There is a paucity of data on the rate of urological and vascular complications in very young children after kidney transplant. We conducted a study on the incidence and risk factors for early post-transplant surgical complications in young recipients (<5 years) over three decades. The primary outcome was any urological or vascular complication within 30 days of transplant, and the secondary outcome was incidence rate of graft failure reported as per 1000 person-years. Risk factors associated with surgical complications were analyzed by logistic regression. There were 22 (26.5%) complications in 21 children with vascular thrombosis being the most common complication. There was no significant difference in the number of complications in period 1 (1985-1994) and period 2 (1995-2014) (P=.1). The incidence rate of graft failure was higher in period 1 (IR 70.8, 95% CI 41.1, 121.9) compared to period 2 (IR 20.7, 95% CI 9.3, 46.0). Cumulative incidence of graft survival at 1, 3, and 5 years' post-transplant was 96.5%, 92.6%, and 90%, respectively, in those without compared to 71%, 65.1%, and 58.6%, respectively, in children with complications. In conclusion, early surgical, especially vascular, complications are quite common in young renal transplant recipients and lead to significantly reduced graft survival.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".