Timing of Pregnancy After Kidney Transplantation and Risk of Allograft Failure
Bibliographic record
Abstract
The optimal timing of pregnancy after kidney transplantation remains uncertain. We determined the risk of allograft failure among women who became pregnant within the first 3 posttransplant years. Among 21 814 women aged 15-45 years who received a first kidney-only transplant between 1990 and 2010 captured in the United States Renal Data System, n = 729 pregnancies were identified using Medicare claims. The probability of allograft failure from any cause including death (ACGL) at 1, 3, and 5 years after pregnancy was 9.6%, 25.9%, and 36.6%. In multivariate analyses, pregnancy in the first posttransplant year was associated with an increased risk of ACGL (hazard ratio [HR]: 1.18; 95% confidence interval [CI] 1.00, 1.40) and death censored graft loss (DCGL) (HR:1.25; 95% CI 1.04, 1.50), while pregnancy in the second posttransplant year was associated with an increased risk of DCGL (HR: 1.26; 95% CI 1.06, 1.50). Pregnancy in the third posttransplant year was not associated with an increased risk of ACGL or DCGL. These findings demonstrate a higher incidence of allograft failure after pregnancy than previously reported and that the increased risk of allograft failure extends to pregnancies in the second posttransplant year.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".