Maternal Health in Autosomal Dominant Tubulointerstitial Kidney Disease
Bibliographic record
Abstract
Background: Autosomal dominant tubulointerstitial kidney disease due to MUC1 mutations (ADTKD-MUC1) and UMOD mutations (ADTKD-UMOD) are becoming increasingly recognized as causes of chronic kidney disease (CKD). Genetic testing allows women to determine if they are affected with these conditions, and data on the outcomes in pregnancy in ADTKD would be of great interest to them as they prepare for future pregnancies. Methods: We surveyed women with ADTKD and genetically unaffected family regarding past pregnancy outcomes. We also analyzed survival to end-stage kidney disease (ESKD) according to number of pregnancies. Results: We received completed standardized questionnaires surveys from 52 women with ADTKD-MUC1 (113 pregnancies), 74 women with ADTKD-UMOD (136 pregnancies), and 35 genetically unaffected women (64 pregnancies). At the time of pregnancy, only 16.5% of genetically affected women were aware that they had ADTKD. Results are summarized in Table 1. There was a nonstatistical increase in HTN and hospitalization for HTN. 10% of births to affected mothers were premature vs. 0% in unaffecteds (p<0.01); 12% of babies required a NICU stay vs. 6% in unaffecteds (p=0.06), but child outcomes were good. Survival analysis showed no statistical differences in age to ESRD based on number of pregnancies for affected women. Conclusions: Patients with ADTKD had an increased prevalence of hypertension, anemia, and early delivery than controls, but overall pregnancy outcomes were good for mother and child. More information is needed on changes in glomerular filtration rate with pregnancy in ADTKD. Funding: Private Foundation Support, Government Support - Non-U.S.Characteristics and Pregnancy Complications Reported.
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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 | 0.000 |
| 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) | 0.002 | 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".