Association of Maternal and Neonatal Birth Outcomes With Subsequent Pediatric Transplants
Bibliographic record
Abstract
BACKGROUND: We identified maternal and neonatal birth characteristics that were associated with organ or tissue transplants during childhood. METHODS: We designed a retrospective cohort study of the population of children born between 2006 and 2019 in Quebec, Canada. The exposure included birth complications such as congenital anomaly, neonatal blood transfusion, and oligohydramnios. The main outcome measure was organ or tissue transplantation before 14 y of age. We categorized transplants according to type (major organs versus superficial tissues). To determine the association of birth characteristics with risk of pediatric transplant, we estimated hazard ratios (HRs) and 95% confidence intervals (CIs) using Cox proportional hazards models adjusted for potential confounders. RESULTS: The cohort comprised 1 038 375 children with 7 712 678 person-years of follow-up, including 436 children who had transplants before 14 y of age. Birth complications were predominantly associated with major organ transplants. Congenital anomaly was associated with heart or lung (HR, 10.41; 95% CI, 5.33-20.33) and kidney transplants (HR, 13.69; 95% CI, 7.48-25.06), compared with no anomaly. Neonatal blood transfusion was associated with all major organ transplants, compared with no transfusion. Maternal complications were not as strongly associated with the risk of childhood transplant, although oligohydramnios was associated with 16.84 times (95% CI, 8.09-35.02) the risk of kidney transplant, compared with no oligohydramnios. CONCLUSIONS: Adverse birth outcomes such as congenital anomaly, neonatal blood transfusion, and maternal oligohydramnios are associated with a greater risk of transplantation before 14 y of age. Maternal and neonatal birth outcomes may be useful predictors of transplantation.
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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 | 0.003 |
| 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 | 0.001 |
| 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".