A Comparison of Pregnancy Outcomes in Women Receiving Intensive Hemodialysis Versus Kidney Transplant Recipients
Bibliographic record
Abstract
Abstract Introduction Tailored hemodialysis prescriptions, including more frequent and intensive regimens, may improve pregnancy outcomes, yet most women are counselled to delay conception until after a successful kidney transplantation. Methods We conducted a retrospective cohort study between 2000-2023 in Toronto, Ontario, Canada to compare pregnancy outcomes of women receiving intensified hemodialysis versus kidney transplant recipients. Results We included 48 pregnancies in 37 women receiving hemodialysis and 96 pregnancies in 60 women conceiving post-kidney transplantation. The two patient populations managed by the same multi-disciplinary team had similar live birth rates (80% in hemodialysis and 76% in transplant patients; p-value 0.68). However, the hemodialysis cohort had a shorter pregnancy duration (36.2 weeks; interquartile range (IQR) 32.5-37.1 versus 37.0 weeks; IQR; 35.7-38.0; p-value 0.004) and smaller infants at birth (2202 grams; IQR 1600-2750 versus 2766 grams; IQR 2380-3180; p-value < 0.001). No difference was found in the proportion of pregnancies with reported pregnancy-associated complications (67% in hemodialysis and 73% in transplant; p-value 0.53), including hypertensive disorders of pregnancy at 33% in the hemodialysis cohort and 47% among transplant recipients (p-value 0.18). Conclusion With appropriate counselling and management, pregnancy on hemodialysis may be considered a viable alternative to conceiving post-renal transplant for women without imminent access to a donor.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".