In one paragraphArticle in Research square, 2023. 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
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed, 2 citations in OpenAlex.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
20 authors at 3 institutions in 2 countries.
Jacquelyn RogerBakar Computational Health Sciences Institute, University of California San Francisco.ORCID 0000-0003-1823-0421 Feng XieDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University.
Jean CostelloBakar Computational Health Sciences Institute, University of California San Francisco.
Tomiko OskotskyBakar Computational Health Sciences Institute, University of California San Francisco.ORCID 0000-0001-7393-5120 Sarah WoldemariamBakar Computational Health Sciences Institute, University of California San Francisco.ORCID 0000-0003-1873-8575 Brian LeBakar Computational Health Sciences Institute, University of California San Francisco.
Dara TorgersonDepartment of Epidemiology and Biostatistics, University of California San Francisco.
Gary M ShawDepartment of Pediatrics, Stanford University.
David K StevensonDepartment of Pediatrics, Stanford University.
M Maria GlymourDepartment of Epidemiology and Biostatistics, University of California San Francisco.ORCID 0000-0001-9644-3081 Hakan CakmakDepartment of Obstetrics and Gynecology, University of California San Francisco.
Ruth B LathiDepartment of Obstetrics and Gynecology, Stanford University.
University of California, San Francisco · USStanford University · USStanford Medicine · US
Funding
Clinical and Translational Science InstituteUL1TR001872 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI COLLARD, HAROLD R, JACOBY, VANESSA · 2016 to 2025
$112.1MMEDICAL SCIENTIST TRAINING PROGRAMT32GM007618 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ANDERSON, MARK S · 1985 to 2020
$29.6MStanford Alzheimer's Disease Research CenterAdmin Supp: Developing iPSC models for AD and PDP30AG066515 · NIA · STANFORD UNIVERSITY · PI Victor Henderson · 2020 to 2026
$29.0MPreeclampsia to cardiovascular disease: Life course analysis of biomarkers and riskR01HL139844 · NHLBI · STANFORD UNIVERSITY · PI HLATKY, MARK A., WINN, VIRGINIA D · 2018 to 2021
$9.0MUCSF Stanford Endometriosis Center for Discovery, Innovation, Training and Community EngagementP01HD106414 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SIROTA, MARINA · 2021 to 2025
$7.1MTrio Analysis of Recurrent Pregnancy Loss Integrated Bioinformatics Genomics Study (TRIOS)R01HD105256 · NICHD · STANFORD UNIVERSITY · PI LATHI, RUTH B, RAJKOVIC, ALEKSANDAR · 2021 to 2025
$7.0MBMI Bioinformatics Training GrantsT32GM067547 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HERNANDEZ, RYAN D. · 2003 to 2022
$6.6MSPRINT: Signature for Pain Recovery IN TeensR61NS114926 · NINDS · STANFORD UNIVERSITY · PI SIMONS, LAURA E · 2019 to 2019
$5.4MDietary Modulation of Neuroinflammation in Age-Related Memory DisordersR01AG058417 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SLOAN, RICHARD P, SMALL, SCOTT A · 2018 to 2022
$4.2MMachine Learning for Integrative Modeling of the Immune System in Clinical SettingsR35GM138353 · NIGMS · STANFORD UNIVERSITY · PI AGHAEEPOUR, NIMA · 2020 to 2024
$2.2MNCATS NIH HHS UL1 TR001872NCBDD CDC HHS U01 DD001226NHLBI NIH HHS R01 HL139844NIA NIH HHS P30 AG066515NIA NIH HHS R01 AG058417NICHD NIH HHS P01 HD106414NICHD NIH HHS R01 HD105256NIGMS NIH HHS R35 GM138353NIGMS NIH HHS T32 GM007618NIGMS NIH HHS T32 GM067547NINDS NIH HHS R61 NS114926
6 · The paper itselfAbstract
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. To generate hypotheses about RPL etiologies, 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 electronic health record databases. In total, our study included 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 were significantly positively associated with RPL in both medical centers. Age-stratified analysis revealed that the majority of RPL-associated diagnoses had higher odds ratios for patients <35 compared with 35+ patients. While Stanford results were sensitive to control for healthcare utilization, UCSF results were stable across analyses with and without utilization. Intersecting significant results between medical centers was an effective filter to identify associations that are robust across center-specific utilization patterns.
Indexed as
clinical phenotypingEMRrecurrent pregnancy loss
Identifiers
PMID36993325
PMCPMC10055527
OpenAlexW4360840139
What OpenQuestion holds
Textmetadata
LicenceCC BY
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