Pregnancy and Glomerular Disease
Bibliographic record
Abstract
During pregnancy, CKD increases both maternal and fetal risk. Adverse maternal outcomes include progression of underlying renal dysfunction, worsening of urine protein, and hypertension, whereas adverse fetal outcomes include fetal loss, intrauterine growth restriction, and preterm delivery. As such, pregnancy in young women with CKD is anxiety provoking for both the patient and the clinician providing care, and because the heterogeneous group of glomerular diseases often affects young women, this is an area of heightened concern. In this invited review, we discuss pregnancy outcomes in young women with glomerular diseases. We have performed a systematic review in attempt to better understand these outcomes among young women with primary GN, we review the studies of pregnancy outcomes in lupus nephritis, and finally, we provide a potential construct for management. Although it is safe to say that the vast majority of young women with glomerular disease will have a live birth, the counseling that we can provide with respect to individualized risk remains imprecise in primary GN because the existing literature is extremely dated, and all management principles are extrapolated primarily from studies in lupus nephritis and diabetes. As such, the study of pregnancy outcomes and management strategies in these rare diseases requires a renewed interest and a dedicated collaborative effort.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".