scholarly journals Treatment Option Criteria for Open Bite with Receiver Operating Characteristic Analysis—A Retrospective Study

2021 ◽  
Vol 11 (18) ◽  
pp. 8736
Author(s):  
Chie Tachiki ◽  
Yasushi Nishii ◽  
Masae Yamamoto ◽  
Takashi Takaki

Temporary anchorage devices (TADs) allow molar intrusion as an additional treatment option to conventional treatment for open bite cases. We investigated the treatment option criteria for open bite treatment. A total of 33 patients with skeletal Class I to Class II open bite who had stable occlusion one year after treatment were enrolled in the study, including 15 patients who had undergone surgical orthodontic treatment, 8 patients who had undergone treatment with molar intrusion, and 10 patients who had undergone treatment with anterior teeth extrusion. Pre-treatment cephalometric analysis of these patients was used for comparison. Furthermore, receiver operating characteristic (ROC) curve analysis was employed to examine the measurement parameters that would be valid as treatment criteria. In the results, FMA showed that patients treated with molar intrusion had a moderately high angle, while those treated with surgical orthodontic treatment had a severe high angle. The area under the curve (AUC) of the ROC curve indicated that FMA is the most appropriate parameter for treatment option criteria. In addition, the cutoff value indicated that the borderline between molar intrusion and surgical orthodontic treatment was 37.5° for FMA. In this study, we suggested criteria for the treatment of open bite with molar intrusion.

Author(s):  
Mario A. Cleves

The area under the receiver operating characteristic (ROC) curve is often used to summarize and compare the discriminatory accuracy of a diagnostic test or modality, and to evaluate the predictive power of statistical models for binary outcomes. Parametric maximum likelihood methods for fitting of the ROC curve provide direct estimates of the area under the ROC curve and its variance. Nonparametric methods, on the other hand, provide estimates of the area under the ROC curve, but do not directly estimate its variance. Three algorithms for computing the variance for the area under the nonparametric ROC curve are commonly used, although ambiguity exists about their behavior under diverse study conditions. Using simulated data, we found similar asymptotic performance between these algorithms when the diagnostic test produces results on a continuous scale, but found notable differences in small samples, and when the diagnostic test yields results on a discrete diagnostic scale.


2020 ◽  
Vol 11 (02) ◽  
pp. 261-266 ◽  
Author(s):  
Ramdas S. Ransing ◽  
Neha Gupta ◽  
Girish Agrawal ◽  
Nilima Mahapatro

Abstract Objective Panic disorder (PD) is associated with changes in platelet and red blood cell (RBC) indices. However, the diagnostic or predictive value of these indices is unknown. This study assessed the diagnostic and discriminating value of platelet and RBC indices in patients with PD. Materials and Methods In this cross-sectional study including patients with PD (n = 98) and healthy controls (n = 102), we compared the following blood indices: mean platelet volume (MPV), platelet distribution width (PDW), and RBC distribution width (RDW). The receiver operating characteristic (ROC) curve was used to calculate the area under the ROC curve (AUC), sensitivity, specificity, and likelihood ratio for the platelet and RBC indices. Results Statistically significant increase in PDW (17.01 ± 0.91 vs. 14.8 ± 2.06; p < 0.0001) and RDW (16.56 ± 2.32 vs. 15.12 ± 2.43; p < 0.0001) levels were observed in patients with PD. PDW and mean corpuscular hemoglobin concentration had larger AUC (0.89 and 0.74, respectively) and Youden’s index (0.65 and 0.39, respectively), indicating their higher predictive capacity as well as higher sensitivity in discriminating patients with PD from healthy controls. Conclusion PDW can be considered a “good” diagnostic or predictive marker in patients with PD.


2000 ◽  
Vol 23 (2) ◽  
pp. 134-139 ◽  
Author(s):  
Vinod Shidham ◽  
Dilip Gupta ◽  
Lorenzo M. Galindo ◽  
Marian Haber ◽  
Carolyn Grotkowski ◽  
...  

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