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Record W4385442874 · doi:10.34067/kid.0000000000000228

Evaluation and Management of Hypertensive Disorders of Pregnancy

2023· review· en· W4385442874 on OpenAlex
Divya Bajpai, Cristina Popa, Prasoon Verma, Sandra M. Dumanski, Silvi Shah

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueKidney360 · 2023
Typereview
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsLibin Cardiovascular Institute of AlbertaAlberta Kidney Disease NetworkUniversity of Calgary
FundersNational Heart, Lung, and Blood InstituteNHLBI Division of Intramural ResearchNational Institutes of Health
KeywordsMedicineLabetalolMethyldopaPregnancyHydralazinePreeclampsiaChronic hypertensionGestational hypertensionNifedipineHypertension in PregnancyObstetricsGestationPediatricsBlood pressureInternal medicine

Abstract

fetched live from OpenAlex

Hypertensive disorders of pregnancy complicate up to 10% of pregnancies and remain the major cause of maternal and neonatal morbidity and mortality. Hypertensive disorders of pregnancy can be classified into four groups depending on the onset of hypertension and the presence of target organ involvement: chronic hypertension, preeclampsia, gestational hypertension, and superimposed preeclampsia on chronic hypertension. Hypertension during pregnancy is associated with a higher risk of cardiovascular disease and kidney failure. Early diagnosis and proper treatment for pregnant women with hypertension remain a priority since this leads to improved maternal and fetal outcomes. Labetalol, nifedipine, methyldopa, and hydralazine are the preferred medications to treat hypertension during pregnancy. In this comprehensive review, we discuss the diagnostic criteria, evaluation, and management of pregnant women with hypertension.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.884
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.101
GPT teacher head0.370
Teacher spread0.269 · 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