Pregnancy in Chronic Kidney Disease: Need for Higher Awareness. A Pragmatic Review Focused on What Could Be Improved in the Different CKD Stages and Phases
Bibliographic record
Abstract
Pregnancy is possible in all phases of chronic kidney disease (CKD), but its management may be difficult and the outcomes are not the same as in the overall population. The prevalence of CKD in pregnancy is estimated at about 3%, as high as that of pre-eclampsia (PE), a better-acknowledged risk for adverse pregnancy outcomes. When CKD is known, pregnancy should be considered as high risk and followed accordingly; furthermore, since CKD is often asymptomatic, pregnant women should be screened for the presence of CKD, allowing better management of pregnancy, and timely treatment after pregnancy. The differential diagnosis between CKD and PE is sometimes difficult, but making it may be important for pregnancy management. Pregnancy is possible, even if at high risk for complications, including preterm delivery and intrauterine growth restriction, superimposed PE, and pregnancy-induced hypertension. Results in all phases are strictly dependent upon the socio-sanitary system and the availability of renal and obstetric care and, especially for preterm children, of intensive care units. Women on dialysis should be aware of the possibility of conceiving and having a successful pregnancy, and intensive dialysis (up to daily, long-hours dialysis) is the clinical choice allowing the best results. Such a choice may, however, need adaptation where access to dialysis is limited or distances are prohibitive. After kidney transplantation, pregnancies should be followed up with great attention, to minimize the risks for mother, child, and for the graft. A research agenda supporting international comparisons is highly needed to ameliorate or provide knowledge on specific kidney diseases and to develop context-adapted treatment strategies to improve pregnancy outcomes in CKD women.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".