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Record W4412177579 · doi:10.1038/s41746-025-01856-z

Leveraging retinal vascular features in non-invasive, early diagnosis of preeclampsia

2025· editorial· en· W4412177579 on OpenAlexaff
Kimia Heydari, Elizabeth J. Enichen, Ben Li, Joseph C. Kvedar

Bibliographic record

Venuenpj Digital Medicine · 2025
Typeeditorial
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRetinalPreeclampsiaOphthalmologyMedicineComputer scienceBiologyPregnancy

Abstract

fetched live from OpenAlex

Preeclampsia, a dangerous hypertensive disorder of third-trimester pregnancy, is a leading cause of maternal and neonatal mortality and morbidity worldwide 1 . It often presents as hypertension, edema, and severe proteinuria, and can escalate to hypertensive emergency and multisystem end-organ dysfunction 1 . Besides adverse peripartum outcomes, preeclampsia carries long-term consequences for women’s cardiometabolic health, including a well-documented association with increased risk for cardiovascular disease, worse quality of life, and shortened life expectancy 2 , 3 . In view of this growing body of evidence, improved preeclampsia risk prediction can help prevent serious peripartum and long-term complications in women.

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 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)0.004
metaresearch head score (Gemma)0.018
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.008
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0050.003

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 head0.010
GPT teacher head0.264
Teacher spread0.253 · 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
GenreEditorial

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
Published2025
Admission routes1
Has abstractyes

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