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Abstract 4362430: Maternal Socioeconomic Status Impacts Diagnosis and Surveillance of Anti-Ro/SSA Positive Pregnancies Complicated by Fetal Cardiac Neonatal Lupus: A Fetal Heart Society (FHS) Research Collaborative Study

2025· article· en· W4415793492 on OpenAlexaff
Stacy A. S. Killen, Alexander Kaizer, Bettina F. Cuneo, Jill P. Buyon, Anita J. Moon‐Grady, Flora Nuñez Gallegos, Lisa K. Hornberger, Lisa Howley, E. Paul, Mary T. Donofrio, Anita Krishnan, Whitnee Hogan, Kavita Sharma, Catherine Ikemba, Stéphanie Levasseur, Sonal T. Owens, Katherine J. DeWeert, Katherine Kohari, Joshua A. Copel, Emily M. Bucholz, Lisa Gilbert, Shardha Srinivasan, Stefani Samples, Angira Patel, Colin K. L. Phoon, Ranjini Srinivasan, Amanda McIntosh, Maria Kiaffas, Miwa Geiger, Carol McFarland, Nelangi M. Pinto, Bhawna Arya, Tam Doan, Chris Lindblade, Melanie R. F. Gropler, Michelle Kaplinski

Bibliographic record

VenueCirculation · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPregnancyCohortSocioeconomic statusGestational ageSocial deprivationFetusSubspecialtyCohort study

Abstract

fetched live from OpenAlex

Introduction: Knowing maternal anti-Ro antibody status prior to pregnancy is important since surveillance can detect early and treatable manifestations of fetal cardiac neonatal lupus (CNL); unfortunately, antibodies are identified after fetal CNL diagnosis in about 50% of cases. Studies have shown that prenatal detection of congenital heart disease falls short in patients of lower socioeconomic status, but effects of social health determinants on timing of anti-Ro diagnosis are unknown. We sought to determine whether neighborhood deprivation was associated with late detection of anti-Ro antibodies. Methods: A retrospective, multicenter, FHS cohort identified fetuses/infants with CNL between January 2013-2024. We compared race, ethnicity, childhood opportunity index (COI, diversitydatakids.org) and proximity to subspecialty care between pregnant persons with prior knowledge of anti-Ro+ and those diagnosed after onset of fetal CNL (atrioventricular block [AVB], cardiomyopathy, valvulitis, hydrops and/or endocardial fibroelastosis). Statistical tests included linear regression, Mann-Whitney, and Fisher’s exact performed in Rv4.2.2. Results: In our cohort of 234 anti-Ro+ pregnancies from 22 centers, 236 fetuses/infants had CNL. Over half (58.6%) were unaware of anti-Ro status at CNL diagnosis. There were no racial or ethnic differences in maternal knowledge of anti-Ro status prior to CNL diagnosis. Neighborhood deprivation (low/very low COI Social/Economic Domain) and public insurance were associated with late detection of anti-Ro antibodies (p=0.024 and p=0.004, respectively). For those with lower COI, distance to maternal-fetal-medicine (MFM, p=0.017) was significantly increased. Gestational age (GA) at CNL diagnosis and proximity to MFM (p=0.002) or cardiology (p=0.027) were significantly associated; for every 10-mile increase in distance to MFM or cardiology, GA at diagnosis increased by 0.14 weeks (95% CI: 0.05-0.23 weeks) or by 0.06 weeks (95% CI: 0.01-0.12 weeks), respectively. Pregnancies with known anti-Ro+ status were diagnosed with CNL 1.5 weeks earlier (p=0.002; 95% CI: 0.6-2.5 weeks). Conclusions: Pregnant persons with public insurance or from lower opportunity neighborhoods were less likely to know their anti-Ro status prior to fetal/infantile CNL diagnosis and at a GA in which rapid treatment could be effective. Universal anti-Ro pregnancy screening could potentially mitigate these disparities.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.345
Teacher spread0.320 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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