cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5666632.v1
title: Effects of socioeconomic position and social mobility on linear growth from early childhood until adolescence

description: ABSTRACT: Objective: To assess the effect of socioeconomic position (SEP) in childhood and social mobility on linear growth through adolescence in a population-based cohort. Methods: Children born in Cuiabá-MT, central-western Brazil, were evaluated during 1994 - 1999. They were first assessed during 1999 - 2000 (0 - 5 years) and again during 2009 - 2011 (10 - 17 years), and their height-for-age was evaluated during these two periods.Awealth index was used to classify the SEP of each child’s family as low, medium, or high. Social mobility was categorized as upward mobility or no upward mobility. Linear mixed models were used. Results: We evaluated 1,716 children (71.4% of baseline) after 10 years, and 60.6% of the families showed upward mobility, with a higher percentage among the lowest economic classes. A higher height-for-age was also observed among those from families with a high SEP both in childhood (low SEP= -0.35 z-score; high SEP= 0.15 z-score, p &lt; 0.01) and adolescence (low SEP= -0.01 z-score; high SEP= 0.45 z-score, p &lt; 0.01), whereas upward mobility did not affect their linear growth. Conclusion: Expressive social mobility was observed, but SEP in childhood and social mobility did not greatly influence linear growth through childhood in this central-western Brazilian cohort.
DOI: 10.6084/m9.figshare.5666632
title: Effects of socioeconomic position and social mobility on linear growth from early childhood until adolescence

description: ABSTRACT: Objective: To assess the effect of socioeconomic position (SEP) in childhood and social mobility on linear growth through adolescence in a population-based cohort. Methods: Children born in Cuiabá-MT, central-western Brazil, were evaluated during 1994 - 1999. They were first assessed during 1999 - 2000 (0 - 5 years) and again during 2009 - 2011 (10 - 17 years), and their height-for-age was evaluated during these two periods.Awealth index was used to classify the SEP of each child’s family as low, medium, or high. Social mobility was categorized as upward mobility or no upward mobility. Linear mixed models were used. Results: We evaluated 1,716 children (71.4% of baseline) after 10 years, and 60.6% of the families showed upward mobility, with a higher percentage among the lowest economic classes. A higher height-for-age was also observed among those from families with a high SEP both in childhood (low SEP= -0.35 z-score; high SEP= 0.15 z-score, p &lt; 0.01) and adolescence (low SEP= -0.01 z-score; high SEP= 0.45 z-score, p &lt; 0.01), whereas upward mobility did not affect their linear growth. Conclusion: Expressive social mobility was observed, but SEP in childhood and social mobility did not greatly influence linear growth through childhood in this central-western Brazilian cohort.
DOI: 10.6084/m9.figshare.5720377
title: Factors associated with adherence and persistence to hormonal therapy in women with breast cancer

description: ABSTRACT: Introduction: Hormonal therapy in breast cancer is essential to the transition from active treatment to care survival, because it improves long-term survival and provides a better quality of life. reducing hospital costs as well. However, adherence and persistence in the recommended treatment are important to achieve the desirable results. Methodology: This is a cohort retrospective study of 182 women on hormonal treatment identified at a high complexity oncology unit, in the southeastern region of Brazil, and followed-up until 2014. We performed a bivariate analysis to analyze the factors associated with adherence and we conducted the multivariate Cox regression to identify variables associated with discontinuity of treatment over time. Results: Overall adherence was 85.2% and persistence was 45.4% at the end of 5 years. No association was found between the studied independent variables and adherence. Women with advanced stage (HR = 2.24; 95% confidence interval 1.45 - 3.45), who did not undergo surgery (HR = 3.46; 95%CI 2.00 - 5.97), and with three or more hospitalizations (HR = 6.06; 95%CI 2.53 - 14.54) exhibited increased risk of discontinuity. Discussion: The variables associated with persistence reflect the relation between the highest disease severity and the discontinuity of adjuvant hormonal treatment. Conclusion: Despite the high adherence level, there is a progressive increase in non-persistence among women on hormonal therapy, influenced by characteristics related to disease severity, which contributes to an inadequate therapeutic response.
DOI: 10.6084/m9.figshare.5720377.v1
title: Factors associated with adherence and persistence to hormonal therapy in women with breast cancer

description: ABSTRACT: Introduction: Hormonal therapy in breast cancer is essential to the transition from active treatment to care survival, because it improves long-term survival and provides a better quality of life. reducing hospital costs as well. However, adherence and persistence in the recommended treatment are important to achieve the desirable results. Methodology: This is a cohort retrospective study of 182 women on hormonal treatment identified at a high complexity oncology unit, in the southeastern region of Brazil, and followed-up until 2014. We performed a bivariate analysis to analyze the factors associated with adherence and we conducted the multivariate Cox regression to identify variables associated with discontinuity of treatment over time. Results: Overall adherence was 85.2% and persistence was 45.4% at the end of 5 years. No association was found between the studied independent variables and adherence. Women with advanced stage (HR = 2.24; 95% confidence interval 1.45 - 3.45), who did not undergo surgery (HR = 3.46; 95%CI 2.00 - 5.97), and with three or more hospitalizations (HR = 6.06; 95%CI 2.53 - 14.54) exhibited increased risk of discontinuity. Discussion: The variables associated with persistence reflect the relation between the highest disease severity and the discontinuity of adjuvant hormonal treatment. Conclusion: Despite the high adherence level, there is a progressive increase in non-persistence among women on hormonal therapy, influenced by characteristics related to disease severity, which contributes to an inadequate therapeutic response.
DOI: 10.6084/m9.figshare.20020413
title: Classification and regression trees for predicting the risk of a negative test result for tuberculosis infection in Brazilian healthcare workers: a cross-sectional study

description: ABSTRACT: Objectives: Healthcare workers (HCWs) have a high risk of acquiring tuberculosis infection (TBI). However, annual testing is resource-consuming. We aimed to develop a predictive model to identify HCWs best targeted for TBI screening. Methodology: We conducted a secondary analysis of previously published results of 708 HCWs working in primary care services in five Brazilian State capitals who underwent two TBI tests: tuberculin skin test and Quantiferon®-TB Gold in-tube. We used a classification and regression tree (CART) model to predict HCWs with negative results for both tests. The performance of the model was evaluated using the receiver operating characteristics (ROC) curve and the area under the curve (AUC), cross-validated using the same dataset. Results: Among the 708 HCWs, 247 (34.9%) had negative results for both tests. CART identified that physician or a community health agent were twice more likely to be uninfected (probability = 0.60) than registered or aid nurse (probability = 0.28) when working less than 5.5 years in the primary care setting. In cross validation, the predictive accuracy was 68% [95% confidence interval (95%CI): 65 - 71], AUC was 62% (95%CI 58 - 66), specificity was 78% (95%CI 74 - 81), and sensitivity was 44% (95%CI 38 - 50). Conclusion: Despite the low predictive power of this model, CART allowed to identify subgroups with higher probability of having both tests negative. The inclusion of new information related to TBI risk may contribute to the construction of a model with greater predictive power using the same CART technique.
DOI: 10.6084/m9.figshare.20020413.v1
title: Classification and regression trees for predicting the risk of a negative test result for tuberculosis infection in Brazilian healthcare workers: a cross-sectional study

description: ABSTRACT: Objectives: Healthcare workers (HCWs) have a high risk of acquiring tuberculosis infection (TBI). However, annual testing is resource-consuming. We aimed to develop a predictive model to identify HCWs best targeted for TBI screening. Methodology: We conducted a secondary analysis of previously published results of 708 HCWs working in primary care services in five Brazilian State capitals who underwent two TBI tests: tuberculin skin test and Quantiferon®-TB Gold in-tube. We used a classification and regression tree (CART) model to predict HCWs with negative results for both tests. The performance of the model was evaluated using the receiver operating characteristics (ROC) curve and the area under the curve (AUC), cross-validated using the same dataset. Results: Among the 708 HCWs, 247 (34.9%) had negative results for both tests. CART identified that physician or a community health agent were twice more likely to be uninfected (probability = 0.60) than registered or aid nurse (probability = 0.28) when working less than 5.5 years in the primary care setting. In cross validation, the predictive accuracy was 68% [95% confidence interval (95%CI): 65 - 71], AUC was 62% (95%CI 58 - 66), specificity was 78% (95%CI 74 - 81), and sensitivity was 44% (95%CI 38 - 50). Conclusion: Despite the low predictive power of this model, CART allowed to identify subgroups with higher probability of having both tests negative. The inclusion of new information related to TBI risk may contribute to the construction of a model with greater predictive power using the same CART technique.
DOI: 10.5517/ccdc.csd.cc27pjl2
title: CCDC 2076274: Experimental Crystal Structure Determination

description: Related Article: Brendan J. Graziano, Thais R. Scott, Matthew V. Vollmer, Michael J. Dorantes, Victor G. Young, Eckhard Bill, Laura Gagliardi, Connie C. Lu|2022|Chemical Science|13|6525|doi:10.1039/D2SC01998A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27pjm3
title: CCDC 2076275: Experimental Crystal Structure Determination

description: Related Article: Brendan J. Graziano, Thais R. Scott, Matthew V. Vollmer, Michael J. Dorantes, Victor G. Young, Eckhard Bill, Laura Gagliardi, Connie C. Lu|2022|Chemical Science|13|6525|doi:10.1039/D2SC01998A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27pjn4
title: CCDC 2076276: Experimental Crystal Structure Determination

description: Related Article: Brendan J. Graziano, Thais R. Scott, Matthew V. Vollmer, Michael J. Dorantes, Victor G. Young, Eckhard Bill, Laura Gagliardi, Connie C. Lu|2022|Chemical Science|13|6525|doi:10.1039/D2SC01998A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27h463
title: CCDC 2070124: Experimental Crystal Structure Determination

description: Related Article: Xin Wang, Zhi-Wei Li, Zi-Jun Huang, Li-Ping Li, Xiao-Ru Peng, Mei-Jie Shang, Pei-Sen Liao, Hsiu-Yi Chao, Gangfeng Ouyang, Gao-Feng Liu|2022|J.Mater.Chem.A|10|11948|doi:10.1039/D1TA08889K
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27pjq6
title: CCDC 2076278: Experimental Crystal Structure Determination

description: Related Article: Brendan J. Graziano, Thais R. Scott, Matthew V. Vollmer, Michael J. Dorantes, Victor G. Young, Eckhard Bill, Laura Gagliardi, Connie C. Lu|2022|Chemical Science|13|6525|doi:10.1039/D2SC01998A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc287vr4
title: CCDC 2092926: Experimental Crystal Structure Determination

description: Related Article: Brendan J. Graziano, Thais R. Scott, Matthew V. Vollmer, Michael J. Dorantes, Victor G. Young, Eckhard Bill, Laura Gagliardi, Connie C. Lu|2022|Chemical Science|13|6525|doi:10.1039/D2SC01998A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27rkvf
title: CCDC 2078235: Experimental Crystal Structure Determination

description: Related Article: Wei-Qiang Zhang, Harry W. T. Morgan, John E. McGrady, Zhong-Ming Sun|2022|Chemical Science|13|6744|doi:10.1039/D2SC01748B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc288rmy
title: CCDC 2093790: Experimental Crystal Structure Determination

description: Related Article: Wei-Qiang Zhang, Harry W. T. Morgan, John E. McGrady, Zhong-Ming Sun|2022|Chemical Science|13|6744|doi:10.1039/D2SC01748B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc28dbst
title: CCDC 2097236: Experimental Crystal Structure Determination

description: Related Article: Xin Wang, Zhi-Wei Li, Zi-Jun Huang, Li-Ping Li, Xiao-Ru Peng, Mei-Jie Shang, Pei-Sen Liao, Hsiu-Yi Chao, Gangfeng Ouyang, Gao-Feng Liu|2022|J.Mater.Chem.A|10|11948|doi:10.1039/D1TA08889K
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc290w4c
title: CCDC 2116002: Experimental Crystal Structure Determination

description: Related Article: Joseph M. Parr, Andrew J. P. White, Mark R. Crimmin|2022|Chemical Science|13|6592|doi:10.1039/D2SC02063G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc290w5d
title: CCDC 2116003: Experimental Crystal Structure Determination

description: Related Article: Joseph M. Parr, Andrew J. P. White, Mark R. Crimmin|2022|Chemical Science|13|6592|doi:10.1039/D2SC02063G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc290w6f
title: CCDC 2116004: Experimental Crystal Structure Determination

description: Related Article: Joseph M. Parr, Andrew J. P. White, Mark R. Crimmin|2022|Chemical Science|13|6592|doi:10.1039/D2SC02063G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc290w7g
title: CCDC 2116005: Experimental Crystal Structure Determination

description: Related Article: Joseph M. Parr, Andrew J. P. White, Mark R. Crimmin|2022|Chemical Science|13|6592|doi:10.1039/D2SC02063G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc290w8h
title: CCDC 2116006: Experimental Crystal Structure Determination

description: Related Article: Joseph M. Parr, Andrew J. P. White, Mark R. Crimmin|2022|Chemical Science|13|6592|doi:10.1039/D2SC02063G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc290w9j
title: CCDC 2116007: Experimental Crystal Structure Determination

description: Related Article: Joseph M. Parr, Andrew J. P. White, Mark R. Crimmin|2022|Chemical Science|13|6592|doi:10.1039/D2SC02063G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc290wbk
title: CCDC 2116008: Experimental Crystal Structure Determination

description: Related Article: Joseph M. Parr, Andrew J. P. White, Mark R. Crimmin|2022|Chemical Science|13|6592|doi:10.1039/D2SC02063G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc290wcl
title: CCDC 2116009: Experimental Crystal Structure Determination

description: Related Article: Joseph M. Parr, Andrew J. P. White, Mark R. Crimmin|2022|Chemical Science|13|6592|doi:10.1039/D2SC02063G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc290wdm
title: CCDC 2116010: Experimental Crystal Structure Determination

description: Related Article: Joseph M. Parr, Andrew J. P. White, Mark R. Crimmin|2022|Chemical Science|13|6592|doi:10.1039/D2SC02063G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc296lty
title: CCDC 2121510: Experimental Crystal Structure Determination

description: Related Article: Ying Zhang, Jia Yuan, Guanglong Huang, Hong Yu, Jinpeng Liu, Jian Chen, Sixuan Meng, Jian-Ji Zhong, Li Dang, Guang-Ao Yu, Chi-Ming Che|2022|Chemical Science|13|6519|doi:10.1039/D2SC00036A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2999rp
title: CCDC 2124112: Experimental Crystal Structure Determination

description: Related Article: Shubhendu S. Karandikar, Avik Bhattacharjee, Bryan E. Metze, Nicole Javaly, Edward J. Valente, Theresa M. McCormick, David R. Stuart|2022|Chemical Science|13|6532|doi:10.1039/D2SC02332F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2999sq
title: CCDC 2124113: Experimental Crystal Structure Determination

description: Related Article: Shubhendu S. Karandikar, Avik Bhattacharjee, Bryan E. Metze, Nicole Javaly, Edward J. Valente, Theresa M. McCormick, David R. Stuart|2022|Chemical Science|13|6532|doi:10.1039/D2SC02332F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.