Pregnancy Outcomes in Women with Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Pregnant women with chronic kidney disease (CKD) are at risk of adverse maternal and fetal outcomes. We conducted a systematic review of observational studies that described this risk. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We searched several databases from their date of inception through June 2010 for eligible articles published in any language. We included any study that reported maternal or fetal outcomes in at least five pregnant women in each group with or without CKD. We excluded pregnant women with a history of transplantation or maintenance dialysis. RESULTS: We identified 13 studies. Adverse maternal events including gestational hypertension, pre-eclampsia, eclampsia, and maternal mortality were reported in 12 studies. There were 312 adverse maternal events among 2682 pregnancies in women with CKD (weighted average of 11.5%) compared with 500 events in 26,149 pregnancies in normal healthy women (weighted average of 2%). One or more adverse fetal outcomes such as premature births, intrauterine growth restriction, small for gestational age, neonatal mortality, stillbirths, and low birth weight were reported in nine of the included studies. Overall, the risk of developing an adverse fetal outcome was at least two times higher among women with CKD compared with those without. CONCLUSIONS: This review summarizes current available evidence to guide physicians in their decision-making, advice, and care for pregnant women with CKD. Additional studies are needed to better characterize the risks.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".