Overview of Cotinine Cutoff Values for Smoking Status Classification

2019 ◽  
pp. 419-431
Author(s):  
Sungroul Kim
2011 ◽  
Vol 60 (3) ◽  
pp. 331-336 ◽  
Author(s):  
Kazuto Matsunaga ◽  
Tsunahiko Hirano ◽  
Keiichiro Akamatsu ◽  
Akira Koarai ◽  
Hisatoshi Sugiura ◽  
...  

Author(s):  
Kazuto Matsunaga ◽  
Tsunahiro Hirano ◽  
Keiichiro Akamatsu ◽  
Akira Koarai ◽  
Hisatoshi Sugiura ◽  
...  

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Joanna Stragierowicz ◽  
Karolina Mikołajewska ◽  
Marta Zawadzka-Stolarz ◽  
Kinga Polańska ◽  
Danuta Ligocka

Setting appropriate cutoff values and the use of a highly sensitive analytical method allow for correct classification of the smoking status. Urine-saliva pairs samples of pregnant women in the second and third trimester, and saliva only in the first trimester were collected. Offline SPE and LC-ESI-MS/MS method was developed in the broad concentration range (saliva 0.4–1000 ng/mL, urine 0.8–4000 ng/mL). The mean recoveries were3.7±7.6% for urine and99.1±2.6% for saliva. LOD for saliva was 0.12 ng/mL and for urine 0.05 ng/mL; LOQ was 0.4 ng/mL and 0.8 ng/mL, respectively. Intraday and interday precision equaled, respectively, 1.2% and 3.4% for urine, and 2.3% and 6.4% for saliva. There was a strong correlation between salivary cotinine and the uncorrected cotinine concentration in urine in the second and third trimesters of pregnancy. The cutoff values were established for saliva 12.9 ng/mL and urine 42.3 ng/mL or 53.1 μg/g creatinine with the ROC curve analysis. The developed analytical method was successfully applied to quantify cotinine, and a significant correlation between the urinary and salivary cotinine levels was found. The presented cut-off values for salivary and urinary cotinine ensure a categorization of the smoking status among pregnant women that is more accurate than self-reporting.


2011 ◽  
Vol 81 (1) ◽  
pp. 43-48 ◽  
Author(s):  
Rahime Bedir Findik ◽  
Nurcihan Karakurt Hascelik ◽  
Kadir Okhan Akin ◽  
Ayse Nurcan Unluer ◽  
Jale Karakaya

Background: Striae gravidarum, a clinical condition commonly seen in pregnant women, produces serious cosmetic problems and may lead to psychological problems. Aim: The present study investigated whether there was any relation between the presence of striae in primigravid pregnant women and blood vitamin C levels, and factors thought to contribute to the formation of striae such as family history, weight gained during pregnancy, smoking status, abdominal and thigh circumference, and age. Methods: Overall, 69 primigravid women attending routine antenatal follow-up and, using prophylactic iron and vitamin preparations, underwent investigation. All were pregnant 36 or more weeks. Scoring was based on striae examination and whether striae were present. The relation between the presence of striae, vitamin C blood levels, and other factors was investigated. Results and Conclusions: Multiple logistic regression analysis showed a significant relation between the presence of striae and blood vitamin C levels (p = 0.046) and between the presence of striae and family history (p = 0.023). No significant relation was found between the presence of striae and age, weight gained during pregnancy, abdominal and thigh circumference, or smoking status. It was concluded that further, more comprehensive studies on the issue are required.


Methodology ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 188-196 ◽  
Author(s):  
Esther T. Beierl ◽  
Markus Bühner ◽  
Moritz Heene

Abstract. Factorial validity is often assessed using confirmatory factor analysis. Model fit is commonly evaluated using the cutoff values for the fit indices proposed by Hu and Bentler (1999) . There is a body of research showing that those cutoff values cannot be generalized. Model fit does not only depend on the severity of misspecification, but also on nuisance parameters, which are independent of the misspecification. Using a simulation study, we demonstrate their influence on measures of model fit. We specified a severe misspecification, omitting a second factor, which signifies factorial invalidity. Measures of model fit showed only small misfit because nuisance parameters, magnitude of factor loadings and a balanced/imbalanced number of indicators per factor, also influenced the degree of misfit. Drawing from our results, we discuss challenges in the assessment of factorial validity.


2012 ◽  
Author(s):  
G. L. Whembolua ◽  
J. T. Davis ◽  
L. R. Reitzel ◽  
H. Guo ◽  
J. L. Thomas ◽  
...  

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