Evidence map›Paper›PMID 41660242›Full record

ArticleiScience2026

Leveraging health records to identify diagnoses associated with recurrent pregnancy loss across two medical centers.

Jacquelyn Roger, Feng Xie, Jean M Costello, Alice S Tang, Jay Liu, Tomiko T Oskotsky, Sarah R Woldemariam, Idit Kosti, Brian L Le, Michael P Snyder and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

20 authors.

Jacquelyn RogerBiological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA.
Feng XieDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA.
Jean M CostelloBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Alice S TangBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Jay LiuBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Tomiko T OskotskyBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Sarah R WoldemariamBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Idit KostiBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Brian L LeBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Michael P SnyderDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Linda C GiudiceDepartment of Obstetrics and Gynecology, University of California, San Francisco, San Francisco, CA, USA.
Gary M ShawDepartment of Pediatrics, Stanford University, Stanford, CA, USA.
David K StevensonDepartment of Pediatrics, Stanford University, Stanford, CA, USA.
Aleksandar RajkovicDepartment of Pathology, University of California, San Francisco, San Francisco, CA, USA.
M Maria GlymourDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
Dara TorgersonDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
Nima AghaeepourDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA.
Hakan CakmakDepartment of Obstetrics and Gynecology, University of California, San Francisco, San Francisco, CA, USA.
Ruth B LathiDepartment of Obstetrics and Gynecology, Stanford University, Stanford, CA, USA.
Marina SirotaBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.

Funding

Clinical and Translational Science InstituteUL1TR001872 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI COLLARD, HAROLD R, JACOBY, VANESSA · 2016 to 2025
$112.1M
NCATS NIH HHS UL1 TR001872
6 · The paper itself

Abstract

Recurrent pregnancy loss (RPL), defined as 2 or more pregnancy losses, affects 5-6% of ever-pregnant individuals. Approximately half of these cases have no identifiable explanation. In this study, we aim to identify diagnoses associated with RPL and generate hypotheses about RPL etiology utilizing electronic health record (EHR) data. We implemented a case-control study comparing the history of over 1,600 diagnoses between RPL and live birth patients, leveraging the University of California San Francisco (UCSF) and Stanford University EHR databases. In total, our study includes 8,496 RPL (UCSF: 3,840, Stanford: 4,656) and 53,278 control (UCSF: 17,259, Stanford: 36,019) patients. Menstrual abnormalities and infertility-associated diagnoses are significantly positively associated with RPL in both medical centers. Age-stratified analysis revealed that the majority of RPL-associated diagnoses have higher odds ratios for patients <35 years compared with 35+ years patients. While Stanford results are sensitive to control for healthcare utilization, UCSF results are stable across analyses with and without utilization.

Indexed as

Health sciencesMedicine

Identifiers

PMID41660242
PMCPMC12877842

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.