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Record W4306949823 · doi:10.1177/1753495x221133150

Maternal health and pregnancy outcomes in autosomal dominant tubulointerstitial kidney disease

2022· article· en· W4306949823 on OpenAlexaff
Anthony J. Bleyer, Kendrah Kidd, Adrienne H. Williams, Emily Johnson, Victoria Robins, Lauren Martin, Abbigail Taylor, Alice Kim, Isai G. Bowline, Dervla M. Connaughton, Carl D. Langefeld, Martina Živná, Stanislav Kmoch

Bibliographic record

VenueObstetric Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsLondon Health Sciences CentreWestern University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesMinisterstvo Zdravotnictví Ceské RepublikyUniverzita Karlova v Praze
KeywordsMedicinePregnancyKidney diseaseObstetricsDiseaseGestationGynecologyInternal medicine

Abstract

fetched live from OpenAlex

Introduction Autosomal dominant tubulointerstitial kidney disease (ADTKD) is an increasingly recognized cause of chronic kidney disease. ADTKD pregnancy outcomes have not previously been described. Methods A cross-sectional survey was sent to women from ADTKD families. Results Information was obtained from 85 afffected women (164 term pregnancies) and 23 controls (50 pregnancies). Only 16.5% of genetically affected women knew they had ADTKD during pregnancy. Eighteen percent of ADTKD mothers had hypertension during pregnancy versus 12% in controls ( p = 0.54) and >40% in comparative studies of chronic kidney disease in pregnancy. Eleven percent of births of ADTKD mothers were <37 weeks versus 0 in controls ( p < 0.0001). Cesarean section occurred in 19% of pregnancies in affected women versus 38% of unaffected individuals ( p = 0.06). Only 12% of babies required a neonatal intensive care unit stay. Conclusions ADTKD pregnancies had lower rates of hypertension during pregnancy versus other forms of chronic kidney disease, which may have contributed to good maternal and fetal outcomes.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.282
Teacher spread0.267 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2022
Admission routes1
Has abstractyes

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