Use of Data Analysis Methods in Dental Publications: Is There Evidence of a Methodological Change?

Nieminen, Pentti; Vähänikkilä, Hannu

doi:10.3390/publications8010009

Cited by 6 publications

(6 citation statements)

References 38 publications

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“…To the best of our knowledge, this is the first use of machine-learning algorithms to predict complete and incremental tooth loss based on socioeconomic and medical health characteristics [22]. In this study, we used national data to develop and test the performance of five…”

Section: Discussionmentioning

confidence: 99%

Predictors of tooth loss: A machine learning approach

et al. 2021

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Introduction Little is understood about the socioeconomic predictors of tooth loss, a condition that can negatively impact individual’s quality of life. The goal of this study is to develop a machine-learning algorithm to predict complete and incremental tooth loss among adults and to compare the predictive performance of these models. Methods We used data from the National Health and Nutrition Examination Survey from 2011 to 2014. We developed multiple machine-learning algorithms and assessed their predictive performances by examining the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values. Results The extreme gradient boosting trees presented the highest performance in the prediction of edentulism (AUC = 88.7%; 95%CI: 87.1, 90.2), the absence of a functional dentition (AUC = 88.3% 95%CI: 87.3,89.3) and for predicting missing any tooth (AUC = 83.2%; 95%CI, 82.0, 84.4). Although, as expected, age and routine dental care emerged as strong predictors of tooth loss, the machine learning approach identified additional predictors, including socioeconomic conditions. Indeed, the performance of models incorporating socioeconomic characteristics was better at predicting tooth loss than those relying on clinical dental indicators alone. Conclusions Future application of machine-learning algorithm, with longitudinal cohorts, for identification of individuals at risk for tooth loss could assist clinicians to prioritize interventions directed toward the prevention of tooth loss.

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Section: Discussionmentioning

confidence: 99%

Predictors of tooth loss: A machine learning approach

et al. 2021

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“…Letters, brief reports, case reports, narrative reviews, and editorials were excluded from our sample. The sub-field of dentistry and oral health was chosen because we had previously analysed dental journals and studied the application of statistical techniques and quality of reporting in those journals [ 15 , 26 , 27 , 28 ].…”

Section: Methodsmentioning

confidence: 99%

“…Adequate evaluation of statistical reporting and data presentation during the submission stage improves the quality of biomedical articles and journals [ 9 , 10 , 11 , 12 ]. Previous studies have shown that statistical information is reported more detailly, comprehensively, and usefully in eminent medical journals [ 13 , 14 , 15 ]. This is consistent with their detailed guidelines for authors, as well as a more strict review process, including extensive statistical reviewing [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

The Quality of Statistical Reporting and Data Presentation in Predatory Dental Journals Was Lower Than in Non-Predatory Journals

Nieminen

Uribe

2021

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Proper peer review and quality of published articles are often regarded as signs of reliable scientific journals. The aim of this study was to compare whether the quality of statistical reporting and data presentation differs among articles published in ‘predatory dental journals’ and in other dental journals. We evaluated 50 articles published in ‘predatory open access (OA) journals’ and 100 clinical trials published in legitimate dental journals between 2019 and 2020. The quality of statistical reporting and data presentation of each paper was assessed on a scale from 0 (poor) to 10 (high). The mean (SD) quality score of the statistical reporting and data presentation was 2.5 (1.4) for the predatory OA journals, 4.8 (1.8) for the legitimate OA journals, and 5.6 (1.8) for the more visible dental journals. The mean values differed significantly (p < 0.001). The quality of statistical reporting of clinical studies published in predatory journals was found to be lower than in open access and highly cited journals. This difference in quality is a wake-up call to consume study results critically. Poor statistical reporting indicates wider general lower quality in publications where the authors and journals are less likely to be critiqued by peer review.

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“…Emerging complex computational methods can provide impressive prediction models. However, it is unclear how widely these methods are applied in different medical domains [10,11]. This Special Issue includes four articles that focus on these predictive methods.…”

Section: Complex Machine Learning and Deep Learning Predictive Algorithmsmentioning

confidence: 99%