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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Predictive performance of the competing risk model in screening for preeclampsia
D. Wright, Min Yi Tan, N. O’Gorman, et al.
American Journal of Obstetrics and Gynecology (2018) Vol. 220, Iss. 2, pp. 199.e1-199.e13
Open Access | Times Cited: 180

Showing 1-25 of 180 citing articles:

Diagnosis of fetal non‐chromosomal abnormalities on routine ultrasound examination at 11–13 weeks' gestation
Argyro Syngelaki, A. Hammami, Sarah Bower, et al.
Ultrasound in Obstetrics and Gynecology (2019) Vol. 54, Iss. 4, pp. 468-476
Open Access | Times Cited: 279
First trimester preeclampsia screening and prediction
Piya Chaemsaithong, Daljit Singh Sahota, Liona C. Poon
American Journal of Obstetrics and Gynecology (2020) Vol. 226, Iss. 2, pp. S1071-S1097.e2
Closed Access | Times Cited: 238
The competing risk approach for prediction of preeclampsia
D. Wright, A. Wright, K. H. Nicolaides
American Journal of Obstetrics and Gynecology (2019) Vol. 223, Iss. 1, pp. 12-23.e7
Closed Access | Times Cited: 222
Advanced maternal age and adverse pregnancy outcomes
Alexander Frick
Best Practice & Research Clinical Obstetrics & Gynaecology (2020) Vol. 70, pp. 92-100
Closed Access | Times Cited: 178
Prospective evaluation of screening performance of first-trimester prediction models for preterm preeclampsia in an Asian population
Piya Chaemsaithong, Ritsuko Pooh, Mingming Zheng, et al.
American Journal of Obstetrics and Gynecology (2019) Vol. 221, Iss. 6, pp. 650.e1-650.e16
Closed Access | Times Cited: 127
Early prediction of preeclampsia via machine learning
Ivana Marić, Abraham Tsur, Nima Aghaeepour, et al.
American Journal of Obstetrics & Gynecology MFM (2020) Vol. 2, Iss. 2, pp. 100100-100100
Closed Access | Times Cited: 107
A new model for screening for early-onset preeclampsia
Bernat Serra, Manel Mendoza, E. Scazzocchio, et al.
American Journal of Obstetrics and Gynecology (2020) Vol. 222, Iss. 6, pp. 608.e1-608.e18
Open Access | Times Cited: 93
Pravastatin Versus Placebo in Pregnancies at High Risk of Term Preeclampsia
Moritz Döbert, Anna Nektaria Varouxaki, An Chi Mu, et al.
Circulation (2021) Vol. 144, Iss. 9, pp. 670-679
Open Access | Times Cited: 91
Cardiovascular events following pregnancy complicated by pre‐eclampsia with emphasis on comparison between early‐ and late‐onset forms: systematic review and meta‐analysis
A. Dall’Asta, F. D’Antonio, Gabriele Saccone, et al.
Ultrasound in Obstetrics and Gynecology (2020) Vol. 57, Iss. 5, pp. 698-709
Open Access | Times Cited: 80
Implementation of routine first trimester combined screening for pre‐eclampsia: a clinical effectiveness study
G. P. Guy, K. Leslie, Daniel Díaz Gómez, et al.
BJOG An International Journal of Obstetrics & Gynaecology (2020) Vol. 128, Iss. 2, pp. 149-156
Open Access | Times Cited: 79
A machine-learning–based algorithm improves prediction of preeclampsia-associated adverse outcomes
Leon J. Schmidt, J. Rieger, Mark Neznansky, et al.
American Journal of Obstetrics and Gynecology (2022) Vol. 227, Iss. 1, pp. 77.e1-77.e30
Closed Access | Times Cited: 58
Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data
Shilong Li, Zichen Wang, Luciana Vieira, et al.
npj Digital Medicine (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 45
The Role of Endocrine Disrupting Chemicals in Gestation and Pregnancy Outcomes
María Puche-Juárez, Juan M. Toledano, Jorge Moreno‐Fernández, et al.
Nutrients (2023) Vol. 15, Iss. 21, pp. 4657-4657
Open Access | Times Cited: 26
Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores From Clinical and Genetic Risk Factors in Early and Late Pregnancies
Vesela Kovacheva, Braden W Eberhard, Raphael Y. Cohen, et al.
Hypertension (2023) Vol. 81, Iss. 2, pp. 264-272
Open Access | Times Cited: 24
The role of cell-free DNA biomarkers and patient data in the early prediction of preeclampsia: an artificial intelligence model
Asma Khalil, Giovanni Bellesia, Mary E. Norton, et al.
American Journal of Obstetrics and Gynecology (2024) Vol. 231, Iss. 5, pp. 554.e1-554.e18
Closed Access | Times Cited: 9
Aspirin delays the development of preeclampsia
D. Wright, K. H. Nicolaides
American Journal of Obstetrics and Gynecology (2019) Vol. 220, Iss. 6, pp. 580.e1-580.e6
Closed Access | Times Cited: 72
Systematic review of prediction models for gestational hypertension and preeclampsia
Edward Antwi, Mary Amoakoh‐Coleman, Dorice Vieira, et al.
PLoS ONE (2020) Vol. 15, Iss. 4, pp. e0230955-e0230955
Open Access | Times Cited: 70
Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia
Herdiantri Sufriyana, Yu‐Wei Wu, Emily Chia‐Yu Su
EBioMedicine (2020) Vol. 54, pp. 102710-102710
Open Access | Times Cited: 65
Routine ultrasound at 32 vs 36 weeks' gestation: prediction of small‐for‐gestational‐age neonates
Anne-Marie Ciobanu, Naila Zaman Khan, Argyro Syngelaki, et al.
Ultrasound in Obstetrics and Gynecology (2019) Vol. 53, Iss. 6, pp. 761-768
Open Access | Times Cited: 64
Screening for pre‐eclampsia at 11–13 weeks' gestation: use of pregnancy‐associated plasma protein‐A, placental growth factor or both
A. Mazer Zumaeta, A. Wright, Argyro Syngelaki, et al.
Ultrasound in Obstetrics and Gynecology (2020) Vol. 56, Iss. 3, pp. 400-407
Open Access | Times Cited: 61
Prediction of imminent preeclampsia at 35–37 weeks gestation
Anca Marina Ciobanu, A. Wright, Anca Maria Panaitescu, et al.
American Journal of Obstetrics and Gynecology (2019) Vol. 220, Iss. 6, pp. 584.e1-584.e11
Closed Access | Times Cited: 57
Data-Driven Modeling of Pregnancy-Related Complications
Camilo Espinosa, Martin Becker, Ivana Marić, et al.
Trends in Molecular Medicine (2021) Vol. 27, Iss. 8, pp. 762-776
Open Access | Times Cited: 46
Chronic hypertension and superimposed preeclampsia: screening and diagnosis
Nikos A. Kametas, Diane Nzelu, K. H. Nicolaides
American Journal of Obstetrics and Gynecology (2021) Vol. 226, Iss. 2, pp. S1182-S1195
Closed Access | Times Cited: 43
Arterial stiffness for the early prediction of pre‐eclampsia compared with blood pressure, uterine artery Doppler and angiogenic biomarkers: a prospective cohort study
Kim Phan, Yessica-Haydee Gomez, Jessica Gorgui, et al.
BJOG An International Journal of Obstetrics & Gynaecology (2023) Vol. 130, Iss. 8, pp. 932-940
Open Access | Times Cited: 17
Pre‐eclampsia screening in Denmark (PRESIDE): national validation study
I. Riishede, Line Rode, Lene Sperling, et al.
Ultrasound in Obstetrics and Gynecology (2023) Vol. 61, Iss. 6, pp. 682-690
Open Access | Times Cited: 17
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