1.A comparative study on the initial stability of different implants placed above the bone level using resonance frequency analysis.
In Ho KANG ; Chang Whe KIM ; Young Jun LIM ; Myung Joo KIM
The Journal of Advanced Prosthodontics 2011;3(4):190-195
PURPOSE: This study evaluated the initial stability of different implants placed above the bone level in different types of bone. MATERIALS AND METHODS: As described by Lekholm and Zarb, cortical layers of bovine bone specimens were trimmed to a thickness of 2 mm, 1 mm or totally removed to reproduce bone types II, III, and IV respectively. Three Implant system (Branemark System(R) Mk III TiUnite(TM), Straumann Standard Implant SLA(R), and Astra Tech Microthread(TM)-OsseoSpeed(TM)) were tested. Control group implants were placed in level with the bone, while test group implants were placed 1, 2, 3, and 4 mm above the bone level. Initial stability was evaluated by resonance frequency analysis. Data was statistically analyzed by one-way analysis of variance in confidence level of 95%. The effective implant length and the Implant Stability Quotient (ISQ) were compared using simple linear regression analysis. RESULTS: In the control group, there was a significant difference in the ISQ values of the 3 implants in bone types III and IV (P<.05). The ISQ values of each implant decreased with increased effective implant length in all types of bone. In type II bone, the decrease in ISQ value per 1-mm increase in effective implant length of the Branemark and Astra implants was less than that of the Straumann implant. In bone types III and IV, this value in the Astra implant was less than that in the other 2 implants. CONCLUSION: The initial stability was much affected by the implant design in bone types III, IV and the implant design such as the short pitch interval was beneficial to the initial stability of implants placed above the bone level.
Linear Models
2.Statistical notes for clinical researchers: simple linear regression 3 – residual analysis
Restorative Dentistry & Endodontics 2019;44(1):e11-
No abstract available.
Linear Models
3.Statistical notes for clinical researchers: simple linear regression 1 – basic concepts.
Restorative Dentistry & Endodontics 2018;43(2):e21-
No abstract available.
Linear Models*
4.Statistical notes for clinical researchers: simple linear regression 2 – evaluation of regression line.
Restorative Dentistry & Endodontics 2018;43(3):e34-
No abstract available.
Linear Models*
5.Effect of coloring agent on the color of zirconia.
Kwanghyun KIM ; Kwantae NOH ; Ahran PAE ; Yi Hyung WOO ; Hyeong Seob KIM
The Journal of Korean Academy of Prosthodontics 2017;55(1):18-25
PURPOSE: The aim of this study was to evaluate the effect of two types of coloring agents and the number of application on the color of zirconia. MATERIALS AND METHODS: Monolithic zirconia specimens (15.7 mm × 15.7 mm × 2.0 mm) (n = 33) was prepared and divided into 11 groups. Each experimental group was coded as a1-a5, w1-w5 according to the type of coloring agent and number of application. Specimens with no coloring agent applied were set as control group. The color difference of specimen was measured by using double-beam spectrophotometer, and calculated color difference (ΔE*(ab)), translucency parameter (TP). All data was analyzed with two-way ANOVA, multiple comparison Schéffe test, Pearson correlation and linear regression analysis. RESULTS: As the number of application increased, values of CIE L* was decreased, but values of CIE b* was increased in both coloring agents. However, there was no significant difference on values of translucency parameter. The color difference range of each group was 0.87 ΔE*(ab) to 9.43 ΔE*(ab). CONCLUSION: In this study, type of coloring agent and the number of application did not affect the color difference of zirconia.
Coloring Agents
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Linear Models
6.Overestimation of Vancomycin Clearance by the Linear Regression Formula in Rodvold's Report: Why?.
Infection and Chemotherapy 2014;46(1):62-63
No abstract available.
Linear Models*
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Vancomycin*
7.Analysis of binary classification repeated measurement data with GEE and GLMMs using SPSS software.
Shengli AN ; Yanhong ZHANG ; Zheng CHEN
Journal of Southern Medical University 2012;32(12):1777-1780
OBJECTIVETo analyze binary classification repeated measurement data with generalized estimating equations (GEE) and generalized linear mixed models (GLMMs) using SPSS19.0.
METHODSGEE and GLMMs models were tested using binary classification repeated measurement data sample using SPSS19.0.
RESULTS AND CONCLUSIONCompared with SAS, SPSS19.0 allowed convenient analysis of categorical repeated measurement data using GEE and GLMMs.
Linear Models ; Models, Statistical ; Software
8.Intraocular Pressure with the Mackay-Marg Electronic Applanation Tonometer in Normal Eyes.
Journal of the Korean Ophthalmological Society 1982;23(3):595-600
The measurement of the intraocular pressure were made with the Mackay-Marg electronic applanation tonometer, as compared with standard Goldmann tonometer and Schiotz tonometer in Korean 114 normal eyes. The results were as follows: 1. The mean intraocular pressure of 114 normal eyes was 16.61 +/- 3. 77 mmHg with a Mackay-Marg tonometer. 2. There was significant differances between the Mackay-Marg tonometer and Goldmann tonometric values(p<0.005). The corelation coefficent(r) was 0.975, the linear regression was Y = 1.37 + 1.06X. 3. The standard deviation for Mackay-Marg tonometer was greater thandoldmann readings, and it was about 1.37 mmHg higher than Goldmann's. 4. There was significant differances between the Mackay-Marg tonometer and Schiotz tonometric values(p<0.005). The corelation coefficent(r) was 0.938, the linear regression was Y = 0.82X - 0.07.
Intraocular Pressure*
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Linear Models
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Reading
9.Sleep Irregularity in the Previous Week Influences the First-Night Effect in Polysomnographic Studies.
Da Hye LEE ; Chul Hyun CHO ; Changsu HAN ; Ki Nam BOK ; Jung Ho MOON ; Eunil LEE ; Heon Jeong LEE ; Leen KIM
Psychiatry Investigation 2016;13(2):203-209
OBJECTIVE: The first-night effect is a well-known phenomenon resulting from an individual's maladaptation to the unfamiliar environment of a sleep laboratory. However, there have been no direct reports of the effect of previous sleep patterns on the first-night effect. We aimed to investigate the effect the previous week's sleep pattern on the first-night effect. METHODS: Twenty-four young, healthy, male participants completed the study procedure. During one week prior to study, the participants kept sleep diaries and wore actigraphs to identify sleep-wake pattern. Two consecutive nights of polysomnography were conducted after that. Wilcoxon signed-rank tests were applied to compare sleep variables of the two nights. Variance (standard deviation) of sleep onset time during the previous week was used as an index of irregularity. A Kendall's ranked correlation analysis and a linear regression test were applied to detect correlation between sleep irregularity and the first-night effect measured by polysomnography. RESULTS: There were significant differences in the values of sleep efficiency (p=0.011) and wake after sleep onset (WASO) (p=0.006) between the two nights. Sleep efficiency was lower and WASO was higher on the first night as compared to the second night. Sleep irregularity in the previous week was negatively correlated with sleep efficiency (p<0.001) of the first night, but was not significantly correlated with any other sleep parameters. CONCLUSION: We replicated the existence of the first-night effect commonly observed in sleep studies. Sleep irregularity in the previous week may influence the first-night effect in polysomnographic studies.
Humans
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Linear Models
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Male
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Polysomnography
10.Reconstruction of femoral length from fragmentary femora.
Jubilant Kwame ABLEDU ; Eric Bekoe OFFEI ; Casmiel Kwabena OSABUTEY
Anatomy & Cell Biology 2016;49(3):206-209
The reconstruction of femoral length (FL) from fragmentary femora is an essential step in estimating stature from fragmentary skeletal remains in forensic investigations. While regression formulae for doing this have been suggested for several populations, such formulae have not been established for Ghanaian skeletal remains. This study, therefore, seeks to derive regression formulae for reconstruction of FL from fragmentary femora of skeletal samples obtained from Ghana. Six measurements (vertical head diameter, transverse head diameter, bicondylar breadth, epicondylar breadth, sub-trochanteric anterior-posterior diameter, and sub-trochanteric transverse diameter) were acquired from different anatomical portions of the femur and the relationship between each acquired measurement and FL was analyzed using linear regression. The results indicated significantly moderate-to-high correlations (r=0.580–0.818) between FL and each acquired measurement. The error estimates of the regression formulae were relatively low (i.e., standard error of estimate, 13.66–19.28 mm), suggesting that the discrepancies between actual and estimated stature were relatively low. Compared with other measurements, sub-trochanteric transverse diameter was the best estimate of FL. In the absence of a complete femur, the regression formulae based on the assessed measurements may be used to infer FL, from which stature can be estimated in forensic investigations.
Femur
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Ghana
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Head
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Linear Models