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  • 1
    Online Resource
    Online Resource
    MDPI AG ; 2011
    In:  Education Sciences Vol. 1, No. 1 ( 2011-02-11), p. 1-3
    In: Education Sciences, MDPI AG, Vol. 1, No. 1 ( 2011-02-11), p. 1-3
    Type of Medium: Online Resource
    ISSN: 2227-7102
    Language: English
    Publisher: MDPI AG
    Publication Date: 2011
    detail.hit.zdb_id: 2704213-3
    SSG: 5,3
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  • 2
    Online Resource
    Online Resource
    MDPI AG ; 2015
    In:  Education Sciences Vol. 5, No. 3 ( 2015-07-31), p. 220-220
    In: Education Sciences, MDPI AG, Vol. 5, No. 3 ( 2015-07-31), p. 220-220
    Type of Medium: Online Resource
    ISSN: 2227-7102
    Language: English
    Publisher: MDPI AG
    Publication Date: 2015
    detail.hit.zdb_id: 2704213-3
    SSG: 5,3
    Location Call Number Limitation Availability
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  • 3
    In: International Journal of Environmental Research and Public Health, MDPI AG, Vol. 19, No. 24 ( 2022-12-07), p. 16419-
    Abstract: (1) Importance: Alzheimer’s disease (AD) is complex and only partially understood. Analyzing the relationship between other more treatable or preventable diseases and AD may help in the prevention and the eventual development of treatments for AD. Risk estimation in a high-risk population, rather than a population already affected with AD, may reduce some bias in risk estimates. (2) Objective: To examine the rates of various comorbidities and cancers in individuals at high-risk for AD, but without a clinical diagnosis, relative to individuals from the same population with normal AD risk. (3) Design, Setting, and Participants: We conducted a study using data from the Utah Population Database (UPDB). The UPDB contains linked data from the Utah Cancer Registry, Utah death certificates, the Intermountain Health patient population, and the University of Utah Health patient population. Subjects were selected based on the availability of ancestral data, linked health information, and self-reported biometrics. (4) Results: In total, 75,877 participants who were estimated to be at high risk for AD based on family history, but who did not have an active AD diagnosis, were analyzed. A lower incidence of diabetes (RR = 0.95, 95% CI [0.92,0.97], p 〈 0.001), hypertension (RR = 0.97, 95% CI [0.95,0.99], p 〈 0.001), and heart disease (RR = 0.95, 95% CI [0.93,0.98], p 〈 0.001) was found. There was no difference in rates of cerebrovascular disease or other forms of dementia. Of the 15 types of cancer analyzed: breast (RR = 1.23, 95% CI [1.16, 1.30], p 〈 0.001); colorectal (RR = 1.30, 95% CI [1.21, 1.39], p 〈 0.001); kidney (RR = 1.49, 95% CI (1.29, 1.72), p 〈 0.001); lung (RR = 1.25, 95% CI [1.13, 1.37], p 〈 0.001); non-Hodgkin’s Lymphoma (RR = 1.29, 95% CI [1.15, 1.44], p 〈 0.001); pancreas (RR = 1.34, 95% CI [1.16, 1.55], p 〈 0.001); stomach (RR = 1.59, 95% CI [1.36, 1.86], p 〈 0.001); and bladder (RR = 1.40, 95% CI [1.25, 1.56], p 〈 0.001), cancers were observed in significant excess among individuals at high-risk for AD after correction for multiple testing. (5) Conclusions and Relevance: Since age is the greatest risk factor for the development of AD, individuals who reach more advanced ages are at increased risk of developing AD. Consistent with this, people with fewer comorbidities earlier in life are more likely to reach an age where AD becomes a larger risk. Our findings show that individuals at high risk for AD have a decreased incidence of various other diseases. This is further supported by our finding that our high-risk group was also found to have an increased incidence of various cancers, which also increase in risk with age. There is the possibility that a more meaningful or etiological relationship exists among these various comorbidities. Further research into the etiological relationship between AD and these comorbidities may elucidate these possible interactions.
    Type of Medium: Online Resource
    ISSN: 1660-4601
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2175195-X
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  • 4
    In: Diagnostics, MDPI AG, Vol. 13, No. 17 ( 2023-08-23), p. 2740-
    Abstract: In the field of orthodontics, providing patients with accurate treatment time estimates is of utmost importance. As orthodontic practices continue to evolve and embrace new advancements, incorporating machine learning (ML) methods becomes increasingly valuable in improving orthodontic diagnosis and treatment planning. This study aimed to develop a novel ML model capable of predicting the orthodontic treatment duration based on essential pre-treatment variables. Patients who completed comprehensive orthodontic treatment at the Indiana University School of Dentistry were included in this retrospective study. Fifty-seven pre-treatment variables were collected and used to train and test nine different ML models. The performance of each model was assessed using descriptive statistics, intraclass correlation coefficients, and one-way analysis of variance tests. Random Forest, Lasso, and Elastic Net were found to be the most accurate, with a mean absolute error of 7.27 months in predicting treatment duration. Extraction decision, COVID, intermaxillary relationship, lower incisor position, and additional appliances were identified as important predictors of treatment duration. Overall, this study demonstrates the potential of ML in predicting orthodontic treatment duration using pre-treatment variables.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662336-5
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