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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:

Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis
Upul Cooray, Richard G. Watt, Georgios Tsakos, et al.
Social Science & Medicine (2021) Vol. 291, pp. 114486-114486
Open Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

The use of ICTs and income distribution in Brazil: A machine learning explanation using SHAP values
Gabriel Paes Herrera, Michel Constantino, Jen‐Je Su, et al.
Telecommunications Policy (2023) Vol. 47, Iss. 8, pp. 102598-102598
Open Access | Times Cited: 15
Application of machine learning in measurement of ageing and geriatric diseases: a systematic review
A. Das, Preeti Dhillon
BMC Geriatrics (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 10
Identifying predictors of the tooth loss phenotype in a large periodontitis patient cohort using a machine learning approach
Chun‐Teh Lee, Kai Zhang, Wen Li, et al.
Journal of Dentistry (2024) Vol. 144, pp. 104921-104921
Closed Access | Times Cited: 3
Machine learning to predict untreated dental caries in adolescents
Rafael Aiello Bomfim
BMC Oral Health (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 3
Research trends in lung cancer and the tumor microenvironment: a bibliometric analysis of studies published from 2014 to 2023
Zhilan Huang, Tingyi Xie, Wei Xie, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 3
Relationship between the number of lost teeth and the occurrence of depressive symptoms in middle-aged adults: a cross-sectional study
Martyna Głuszek–Osuch, Elżbieta Cieśla, Edyta Suliga
BMC Oral Health (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 2
Identifying predictors of tooth loss using a rule‐based machine learning approach: A retrospective study at university‐setting clinics
Chun‐Teh Lee, Kai Zhang, Wen Li, et al.
Journal of Periodontology (2023) Vol. 94, Iss. 10, pp. 1231-1242
Closed Access | Times Cited: 5
Prediction of mortality risk of health checkup participants using machine learning-based models: the J-SHC study
Kazuharu Kawano, Yoichiro Otaki, Natsuko Suzuki, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 8
Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment
Akira Hasuike, Taito Watanabe, Shin Wakuda, et al.
Journal of Personalized Medicine (2022) Vol. 12, Iss. 10, pp. 1682-1682
Open Access | Times Cited: 8
Childhood Socioeconomic Status Affects Dental Pain in Later Life
Takafumi Yamamoto, Upul Cooray, Taro Kusama, et al.
JDR Clinical & Translational Research (2024)
Closed Access | Times Cited: 1
Analysis of factors associated with tooth loss in older adults from 1995 to 2015: a population-based cross-sectional survey in Guangdong, China
Linxin Jiang, Jianbo Li, Meng Luo, et al.
Clinical Oral Investigations (2024) Vol. 28, Iss. 11
Closed Access | Times Cited: 1
A comparative quantitative assessment of 3D-printed PEKK and PEEK thin meshes in customized alveolar bone augmentation
Xinrui Zhu, Chang Chen, Yutong Hua, et al.
BMC Oral Health (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 1
Finite element analysis of stress in oral mucosa and titanium mesh interface
Chenxi Wang, Qiguo Rong, Ning Zhu, et al.
BMC Oral Health (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 4
Education‐related inequalities in oral health among older adults: Comparing Singapore and Japan
Sakura Kiuchi, Jun Aida, Upul Cooray, et al.
Community Dentistry And Oral Epidemiology (2023) Vol. 51, Iss. 4, pp. 671-679
Closed Access | Times Cited: 4
Prediction Model Development and Validation of 12-Year Incident Edentulism of Older Adults in the United States
John S. Preisser, Kevin Moss, Tracy L. Finlayson, et al.
JDR Clinical & Translational Research (2022) Vol. 8, Iss. 4, pp. 384-393
Open Access | Times Cited: 7
Association of a lesser number of teeth with more risk of developing depressive symptoms among middle-aged and older adults in Japan: A 20-year population-based cohort study
Wei‐Min Chu, Yukiko Nishita, Chikako Tange, et al.
Journal of Psychosomatic Research (2023) Vol. 174, pp. 111498-111498
Open Access | Times Cited: 3
An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes
Nazila Ameli, Monica Prasad Gibson, Amreesh Khanna, et al.
Frontiers in Dental Medicine (2022) Vol. 3
Open Access | Times Cited: 4
Incidence of edentulism among older adults using the Korean National Health Insurance Service database, 2013-2018
Hyeonjeong Go, Eun‐Kyong Kim, Hoi‐In Jung, et al.
Epidemiology and Health (2022) Vol. 44, pp. e2022091-e2022091
Open Access | Times Cited: 4
Demand prediction of medical services in home and community-based services for older adults in China using machine learning
Yucheng Huang, Tingke Xu, Qingren Yang, et al.
Frontiers in Public Health (2023) Vol. 11
Open Access | Times Cited: 2
Trend in the Incidence of Severe Partial Edentulism among Adults Using the Korean National Health Insurance Service Claim Data, 2014–2018
Hyeonjeong Go, Hoi‐In Jung, Song Vogue Ahn, et al.
Yonsei Medical Journal (2024) Vol. 65, Iss. 4, pp. 234-234
Open Access
Boosting Winter Green Travel: Prioritizing Built Environment Enhancements for Shared Bike Users Accessing Public Transit in the First/Last Mile Using Machine Learning and Grounded Theory
Yu Du, Ji Xian, Chenxi Dou, et al.
Sustainability (2024) Vol. 16, Iss. 22, pp. 9843-9843
Open Access
Indonesian Tooth Loss Predictor in Middle-aged and Elderly Populations based on Sociodemographic Factors and Systemic Disease: A Cross-Sectional Study
Sri Susilawati, Kurnia Wahyudi, Tince Arniati Jovina, et al.
The Open Dentistry Journal (2024) Vol. 18, Iss. 1
Open Access
Last dental visit and severity of tooth loss: a machine learning approach
Rafael Aiello Bomfim
BMC Research Notes (2023) Vol. 16, Iss. 1
Open Access | Times Cited: 1
Using machine learning to study the association of sociodemographic indicators, biomarkers, and oral condition in older adults in Colombia
Javier Enrique Botero, Ana Isabel Zuluaga, Vanessa Suárez-Córdoba, et al.
The Journal of the American Dental Association (2023) Vol. 154, Iss. 8, pp. 715-726.e5
Closed Access | Times Cited: 1
Application of machine learning in measurement of ageing and geriatric diseases: A systematic review
Ayushi Das, Preeti Dhillon
Research Square (Research Square) (2023)
Open Access
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