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:

Evaluation of the Applicability of Artificial Intelligence for the Prediction of Obstructive Sleep Apnoea
Viktória Molnár, László Kunos, László Tamás, et al.
Applied Sciences (2023) Vol. 13, Iss. 7, pp. 4231-4231
Open Access | Times Cited: 6

Showing 6 citing articles:

The Link Between Sleep-Related Breathing Disorders and Idiopathic Pulmonary Fibrosis: Pathophysiological Mechanisms and Treatment Options—A Review
A. Patsoura, Giulia Baldini, Daniele Puggioni, et al.
Journal of Clinical Medicine (2025) Vol. 14, Iss. 7, pp. 2205-2205
Open Access

Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review
Lucrezia Giorgi, Domiziana Nardelli, Antonio Moffa, et al.
Healthcare (2025) Vol. 13, Iss. 2, pp. 181-181
Open Access

Association of Short Sleep Duration and Obstructive Sleep Apnea with Central Obesity: A Retrospective Study Utilizing Anthropometric Measures
Yi Li, Yixuan Lu, Youdan Zhao, et al.
Nature and Science of Sleep (2024) Vol. Volume 16, pp. 1545-1556
Open Access | Times Cited: 2

A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators
Jiewei Huang, Jiajing Zhuang, Huaxian Zheng, et al.
Nature and Science of Sleep (2024) Vol. Volume 16, pp. 413-428
Open Access | Times Cited: 1

Automatic prediction of obstructive sleep apnea in patients with temporomandibular disorder based on multidata and machine learning
Yeon‐Hee Lee, Seonggwang Jeon, Q‐Schick Auh, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 1

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