Acute kidney injury in pregnancy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pregnancy-related acute kidney injury (Pr-AKI) is associated with increased maternal and fetal morbidity and mortality and remains a large public health problem. RECENT FINDINGS: Pr-AKI incidence has globally decreased over time for the most part. However, the cause presents a disparity between developing and developed countries, reflecting differences in socioeconomic factors and healthcare infrastructure - with the noteworthy outlier of increased incidence in the United States and Canada. Although Pr-AKI can be secondary to conditions affecting the general population, in most cases it is pregnancy specific. Septic abortion, hyperemesis gravidarum, and hemorrhage have become less prevalent with access to healthcare but are being displaced by thrombotic microangiopathies, such as preeclampsia, hemolysis, elevated liver enzymes, low platelets syndrome, thrombotic thrombocytopenic purpura, and pregnancy-associated hemolytic-uremic syndromes, as well as acute fatty liver of pregnancy. Understanding these conditions plays a pivotal role in the timely diagnosis and enhancement of therapeutic approaches. SUMMARY: In this review, we focus on the renal physiology of the pregnancy, epidemiology, and specific conditions known to cause Pr-AKI, summarizing diagnostic definition, insights in pathophysiology, clinical considerations, and novel treatment approaches, thus providing the reader a framework of clinically relevant information for interdisciplinary management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".