Pregnancy Outcomes Following Liver Transplantation: A Two-Centre Retrospective Study [36D]
Bibliographic record
Abstract
INTRODUCTION: This study reports pregnancy outcomes in women following liver transplantation to aid counselling and management of these women. METHODS: We conducted a retrospective study including women with pregnancies following liver transplantation followed at Mount Sinai Hospital, Toronto, Canada and University Hospital Leuven, Belgium between 1989 -2016. Pregnancy outcomes were reported as proportions. RESULTS: We identified 28 women that had 41 pregnancies following liver transplantation. The mean maternal age was 30 (±7) years and transplant-to-pregnancy interval was 8.5 (±5.1) years. Six women had received two liver transplants prior to conception, but had normal liver functions at the start of pregnancy. Immunosuppressants included tacrolimus ± azathioprine (26), cyclosporine (4) and prednisone with immunosuppressants (11). Maternal complications included hypertension (10), renal insufficiency (6), gestational diabetes (4), graft deterioration (2), infection (2) and anemia requiring blood transfusion (1). Fetal/neonatal adverse outcomes included two miscarriages, three stillbirths, one neonatal death, five small-for-gestational-age infants and one minor congenital anomaly. There were 22 term and 14 preterm infants with a mean gestational age of 36+5±4.23 weeks. Although the cesarean delivery rate was high (61%), all were performed for obstetric indications. CONCLUSION: With appropriate multidisciplinary care and with stable graft function at the onset of pregnancy, women with liver transplants can have successful pregnancies with low rates of graft complications, if they adhere to their immunosuppressive regimens. Graft deterioration responds well to increase in the dose of immunosuppressants and high-dose glucocorticoids. The use of these in pregnancy is not associated with adverse fetal/neonatal outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".