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Record W2972799728 · doi:10.1097/mcc.0000000000000656

Acute kidney injury in pregnancy

2019· review· en· W2972799728 on OpenAlexaboutno aff
Madhusudan Vijayan, Maria Avendano, Kana Amari Chinchilla, Belinda Jim

Bibliographic record

VenueCurrent Opinion in Critical Care · 2019
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePregnancyAcute kidney injuryIntensive care medicineAcute fatty liver of pregnancyThrombotic thrombocytopenic purpuraPreeclampsiaEclampsiaIncidence (geometry)PopulationEpidemiologyObstetricsInternal medicineFetusPlatelet

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.715
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.329
GPT teacher head0.566
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations27
Published2019
Admission routes1
Has abstractyes

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