ArticleScientific reports2021
Risk factor assessments of temporomandibular disorders via machine learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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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.
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.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Association between overweight/obesity and oral health in adults aged 55 and older: a systematic review and meta-analysis.GeroScience · 2026Pooled it
- Temporomandibular Disorders Diagnosis: Current Challenges and the Promising Role of Artificial Intelligence.European journal of dentistry · 2026Article
- Artificial Intelligence-Based Approach for Determining the Risk of Temporomandibular Disorders.Journal of oral rehabilitation · 2026Article
- Clinical Findings Related to Temporomandibular Disorders in Schoolchildren Aged 4-16 Years: A Multicenter Cross-Sectional Study.Journal of personalized medicine · 2026Article
- Epidemiology and sociodemographic determinants of chronic temporomandibular disorders in South Korea: a nationwide population-based study.BMC oral health · 2025Article
- Bidirectional causality between female reproductive traits and temporomandibular disorders.Journal of oral & facial pain and headache · 2025Article
- Application of artificial intelligence in the diagnosis and management of temporomandibular joint osteoarthritis using cone-beam computed tomography: An evidence-based systematic review.Imaging science in dentistry · 2025Review
- Using Propensity Score Subclassification to Estimate the Population-Average Causal Effect of Temporomandibular Dysfunction Experience on Oral Health-Related Quality of Life Among Australian Adults.Community dentistry and oral epidemiology · 2025Article
- Causal Links Between Bone Diseases and Temporomandibular Disorders.International dental journal · 2025Article
- Application of machine learning in dentistry: insights, prospects and challenges.Acta odontologica Scandinavica · 2025Review
- Identifying novel risk factors for aneurysmal subarachnoid haemorrhage using machine learning.Scientific reports · 2025Article
- Understanding Occlusion and Temporomandibular Joint Function Using Deep Learning and Predictive Modeling.Clinical and experimental dental research · 2024Review
- Adipose tissue-derived extracellular vesicles aggravate temporomandibular joint osteoarthritis associated with obesity.Clinical and translational medicine · 2024Article
- Associations Between Temporomandibular Disorders and Brain Imaging-Derived Phenotypes.International dental journal · 2024Article
- Comparison of Machine Learning Algorithms Fed with Mobility-Related and Baropodometric Measurements to Identify Temporomandibular Disorders.Sensors (Basel, Switzerland) · 2024Article
- Impact of chronic painful temporomandibular disorders on quality of life.Journal of oral & facial pain and headache · 2024Article
- Causal relationship between hypothyroidism and temporomandibular disorders: evidence from complementary genetic methods.BMC oral health · 2024Article
- Musculoskeletal disorders among dental assistants: a cross-sectional study.BMC musculoskeletal disorders · 2024Article
- Causal associations between sleep traits and temporomandibular disorders: a bidirectional mendelian randomization analysis.Frontiers in genetics · 2024Article
- Genetically predicted major depression causally increases the risk of temporomandibular joint disorders.Frontiers in genetics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to use artificial intelligence to determine whether biological and psychosocial factors, such as stress, socioeconomic status, and working conditions, were major risk factors for temporomandibular disorders (TMDs). Data were retrieved from the fourth Korea National Health and Nutritional Examination Survey (2009), with information concerning 4744 participants' TMDs, demographic factors, socioeconomic status, working conditions, and health-related determinants. Based on variable importance observed from the random forest, the top 20 determinants of self-reported TMDs were body mass index (BMI), household income (monthly), sleep (daily), obesity (subjective), health (subjective), working conditions (control, hygiene, respect, risks, and workload), occupation, education, region (metropolitan), residence type (apartment), stress, smoking status, marital status, and sex. The top 20 determinants of temporomandibular disorders determined via a doctor's diagnosis were BMI, age, household income (monthly), sleep (daily), obesity (subjective), working conditions (control, hygiene, risks, and workload), household income (subjective), subjective health, education, smoking status, residence type (apartment), region (metropolitan), sex, marital status, and allergic rhinitis. This study supports the hypothesis, highlighting the importance of obesity, general health, stress, socioeconomic status, and working conditions in the management of TMDs.
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Registered trials
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.