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Record W4378649738 · doi:10.1038/s41591-023-02374-9

Polygenic prediction of preeclampsia and gestational hypertension

2023· review· en· W4378649738 on OpenAlexaff
Michael C. Honigberg, Buu Truong, Raiyan R. Khan, Brenda Xiao, Laxmi Bhatta, Ha My T. Vy, Rafael F. Guerrero, Art Schuermans, Margaret Sunitha Selvaraj, Aniruddh P. Patel, Satoshi Koyama, So Mi Jemma Cho, Shamsudheen Karuthedath Vellarikkal, Mark Trinder, Sarah Urbut, Kathryn J. Gray, Ben Brumpton, Snehal Patil, Sebastian Zöllner, Mariah C. Antopia, Richa Saxena, Girish N. Nadkarni, Ron Do, Qi Yan, Itsik Pe’er, Shefali S. Verma, Rajat M. Gupta, David M. Haas, Hilary C. Martin, David A. van Heel, Triin Laisk, Pradeep Natarajan

Bibliographic record

VenueNature Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsUniversity of British Columbia
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentNational Heart, Lung, and Blood InstituteMedical Research CouncilGenentechAstraZenecaNational Institute of General Medical SciencesKorea Health Industry Development InstituteMassachusetts General HospitalBoston Scientific CorporationPreeclampsia FoundationFondation LeducqHarvard CatalystNational Institute of Diabetes and Digestive and Kidney DiseasesAmgenNational Human Genome Research InstituteWellcome TrustBelgian American Educational FoundationAmerican Heart AssociationNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsPreeclampsiaGestational hypertensionMedicinePregnancyEclampsiaGenome-wide association studyObstetricsBioinformaticsInternal medicineBiologySingle-nucleotide polymorphismGeneticsGenotype

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.078
GPT teacher head0.353
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations120
Published2023
Admission routes1
Has abstractno

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