Cognitive therapists who treat drug-dependent patients are likely to lose at least 50 percent of their patients to dropout. This chapter has presented a cognitive model for conceptualizing missed sessions and dropout, along with strategies for reducing the likelihood of missed sessions and dropout. The following should serve to highlight these strategies. 1. Therapists are encouraged to offer warm, empathetic, collaborative relationships in which drug-dependent patients can feel accepted, understood, and validated. 2. Therapists are encouraged to develop comprehensive, accurate case conceptualizations, with attention paid to the potential for missed sessions and dropout. Case conceptualizations should ultimately guide cognitive and behavioral techniques. 3. Therapists are encouraged to structure sessions and elicit feedback regarding their patient's thoughts and beliefs about therapy and the therapist. This feedback is facilitated by such questions as, "What do you like most about therapy?" "What do you like least?" "What has changed in your life as a result of therapy?" "How do you view our relationship?" 4. Therapists are encouraged to socialize patients in a timely, appropriate manner. 5. Similar to the process of socialization, therapists are encouraged to use cognitive and behavioral techniques in a timely, appropriate manner. It is unrealistic to think that the problems of missed sessions and dropout from drug treatment will ever be fully resolved. Nonetheless, the authors believe that the conceptual models and fundamental strategies presented in this chapter represent a significant step in addressing these problems.
{"title":"Back to basics: fundamental cognitive therapy skills for keeping drug-dependent individuals in treatment.","authors":"B S Liese, A T Beck","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Cognitive therapists who treat drug-dependent patients are likely to lose at least 50 percent of their patients to dropout. This chapter has presented a cognitive model for conceptualizing missed sessions and dropout, along with strategies for reducing the likelihood of missed sessions and dropout. The following should serve to highlight these strategies. 1. Therapists are encouraged to offer warm, empathetic, collaborative relationships in which drug-dependent patients can feel accepted, understood, and validated. 2. Therapists are encouraged to develop comprehensive, accurate case conceptualizations, with attention paid to the potential for missed sessions and dropout. Case conceptualizations should ultimately guide cognitive and behavioral techniques. 3. Therapists are encouraged to structure sessions and elicit feedback regarding their patient's thoughts and beliefs about therapy and the therapist. This feedback is facilitated by such questions as, \"What do you like most about therapy?\" \"What do you like least?\" \"What has changed in your life as a result of therapy?\" \"How do you view our relationship?\" 4. Therapists are encouraged to socialize patients in a timely, appropriate manner. 5. Similar to the process of socialization, therapists are encouraged to use cognitive and behavioral techniques in a timely, appropriate manner. It is unrealistic to think that the problems of missed sessions and dropout from drug treatment will ever be fully resolved. Nonetheless, the authors believe that the conceptual models and fundamental strategies presented in this chapter represent a significant step in addressing these problems.</p>","PeriodicalId":76229,"journal":{"name":"NIDA research monograph","volume":"165 ","pages":"207-32"},"PeriodicalIF":0.0,"publicationDate":"1997-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"20187203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maximizing the tendency of the survey respondent to answer truthfully when sensitive questions are presented is critical issue in survey methodology. A recent development devoted generally to the reduction of response error in survey data is the use of cognitive laboratory techniques during the survey development phase. The chapter categorizes and describes the various cognitive techniques that have been applied, by Federal agencies and other researchers, to the study of sensitive questions. Based on this analysis and review, a number of recommendations are made concerning specific aspects of survey design, when sensitive questions are administered.
{"title":"The use of the psychological laboratory to study sensitive survey topics.","authors":"G B Willis","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Maximizing the tendency of the survey respondent to answer truthfully when sensitive questions are presented is critical issue in survey methodology. A recent development devoted generally to the reduction of response error in survey data is the use of cognitive laboratory techniques during the survey development phase. The chapter categorizes and describes the various cognitive techniques that have been applied, by Federal agencies and other researchers, to the study of sensitive questions. Based on this analysis and review, a number of recommendations are made concerning specific aspects of survey design, when sensitive questions are administered.</p>","PeriodicalId":76229,"journal":{"name":"NIDA research monograph","volume":"167 ","pages":"416-38"},"PeriodicalIF":0.0,"publicationDate":"1997-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"20186281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Inhalation studies with drugs of abuse.","authors":"Y Meng, A H Lichtman, D T Bridgen, B R Martin","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":76229,"journal":{"name":"NIDA research monograph","volume":"173 ","pages":"201-24"},"PeriodicalIF":0.0,"publicationDate":"1997-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"20202251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Antibodies as pharmacokinetic and metabolic modifiers of neurotoxicity.","authors":"S M Owens","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":76229,"journal":{"name":"NIDA research monograph","volume":"173 ","pages":"259-72"},"PeriodicalIF":0.0,"publicationDate":"1997-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"20202253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
W. Ling, S. Shoptaw, D. Wesson, R. Rawson, Margaret A. Compton, Klett Cj
A variety of measures are used for evaluating patients’ responses to substance abuse treatments. These range from physical measures (such as samples of urine, breath, hair, or blood), self-reports of drug use (such as the Addiction Severity Index (ASI) or the Time Line Follow-Back), self-reports of psychological or physiological functioning (such as symptom checklists or craving or mood ratings), and collateral reports. Physical indices of recent drug use, such as urine toxicology screens, are preferable to self-report or collateral reports for evaluating patients’ responses to drug abuse treatments because of their objectivity. In order to optimize the likelihood of both detecting individual episodes of problem drug use and correctly inferring drug abstinence based on urine toxicology results, guidelines have been suggested for collection procedures and timing for collection of urine specimens (Blaine et al. 1994; Cone and Dickerson 1992; Jain 1992). However, the difficult task of aggregating urine toxicology results remains, whether when interpreting the response of a single patient to a specific treatment or when evaluating a treatment’s effectiveness based on a group of patients’ responses in a clinical trial. Difficulties in aggregating urine toxicology results include, but certainly would not be limited to, such problems as the frequency and sensitivity of toxicology screens, early termination of some patients from treatment (or, conversely, the continued participation of some patients who respond poorly to treatment), and problems of analyzing a data matrix that contains a large number of missing datapoints. This chapter reviews the objective indices of treatment response that have traditionally been used and suggests three composite methods for evaluating these data: the Treatment Effectiveness Score (TES), the Joint Probability score (JP), and the Clinical Stabilization Score (CSS).
各种措施被用来评估病人对药物滥用治疗的反应。这些范围从物理测量(如尿液、呼吸、头发或血液样本)，药物使用的自我报告(如成瘾严重程度指数(ASI)或时间线追踪)，心理或生理功能的自我报告(如症状清单或渴望或情绪评级)，以及附带报告。近期药物使用的物理指标，如尿液毒理学筛查，因其客观性而优于自我报告或附带报告，以评估患者对药物滥用治疗的反应。为了优化发现问题药物使用的个体事件和根据尿液毒理学结果正确推断药物戒断的可能性，已经提出了收集尿液标本的程序和时间指南(Blaine等人，1994;Cone and Dickerson 1992;耆那教的1992)。然而，汇总尿液毒理学结果的艰巨任务仍然存在，无论是在解释单个患者对特定治疗的反应时，还是在临床试验中基于一组患者的反应来评估治疗的有效性时。汇总尿液毒理学结果的困难包括，但肯定不限于，毒理学筛查的频率和敏感性，一些患者早期终止治疗(或者相反，一些对治疗反应不佳的患者继续参与治疗)，以及分析包含大量缺失数据点的数据矩阵的问题。本章回顾了传统上使用的治疗反应的客观指标，并提出了评估这些数据的三种综合方法:治疗效果评分(TES)、联合概率评分(JP)和临床稳定评分(CSS)。
{"title":"Treatment effectiveness score as an outcome measure in clinical trials.","authors":"W. Ling, S. Shoptaw, D. Wesson, R. Rawson, Margaret A. Compton, Klett Cj","doi":"10.1037/e495552006-011","DOIUrl":"https://doi.org/10.1037/e495552006-011","url":null,"abstract":"A variety of measures are used for evaluating patients’ responses to substance abuse treatments. These range from physical measures (such as samples of urine, breath, hair, or blood), self-reports of drug use (such as the Addiction Severity Index (ASI) or the Time Line Follow-Back), self-reports of psychological or physiological functioning (such as symptom checklists or craving or mood ratings), and collateral reports. Physical indices of recent drug use, such as urine toxicology screens, are preferable to self-report or collateral reports for evaluating patients’ responses to drug abuse treatments because of their objectivity. In order to optimize the likelihood of both detecting individual episodes of problem drug use and correctly inferring drug abstinence based on urine toxicology results, guidelines have been suggested for collection procedures and timing for collection of urine specimens (Blaine et al. 1994; Cone and Dickerson 1992; Jain 1992). However, the difficult task of aggregating urine toxicology results remains, whether when interpreting the response of a single patient to a specific treatment or when evaluating a treatment’s effectiveness based on a group of patients’ responses in a clinical trial. Difficulties in aggregating urine toxicology results include, but certainly would not be limited to, such problems as the frequency and sensitivity of toxicology screens, early termination of some patients from treatment (or, conversely, the continued participation of some patients who respond poorly to treatment), and problems of analyzing a data matrix that contains a large number of missing datapoints. This chapter reviews the objective indices of treatment response that have traditionally been used and suggests three composite methods for evaluating these data: the Treatment Effectiveness Score (TES), the Joint Probability score (JP), and the Clinical Stabilization Score (CSS).","PeriodicalId":76229,"journal":{"name":"NIDA research monograph","volume":"175 1","pages":"208-20"},"PeriodicalIF":0.0,"publicationDate":"1997-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57798737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Choice of the procedures and types of cognitive-neuromotor testing used in assessment of cocaine abusers and their treatment is dependent on a clear definition of the purposes of testing and the characteristics of the individual tests. This chapter will first discuss published studies of testing in cocaine abusers and pharmacodynamic effects of stimulants and withdrawal. The types of tests available and their characteristics will be discussed in terms of the purpose of testing. The case will be made for the value of computerized cognitiveneuromotor testing when repeated assessment is needed in a busy clinical setting.
{"title":"Cognitive-neuromotor assessment of substance abuse: focus on issues related to cocaine abuse treatment.","authors":"E. Ellinwood, T. Lee","doi":"10.1037/E495552006-010","DOIUrl":"https://doi.org/10.1037/E495552006-010","url":null,"abstract":"Choice of the procedures and types of cognitive-neuromotor testing used in assessment of cocaine abusers and their treatment is dependent on a clear definition of the purposes of testing and the characteristics of the individual tests. This chapter will first discuss published studies of testing in cocaine abusers and pharmacodynamic effects of stimulants and withdrawal. The types of tests available and their characteristics will be discussed in terms of the purpose of testing. The case will be made for the value of computerized cognitiveneuromotor testing when repeated assessment is needed in a busy clinical setting.","PeriodicalId":76229,"journal":{"name":"NIDA research monograph","volume":"175 1","pages":"192-207"},"PeriodicalIF":0.0,"publicationDate":"1997-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57799170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The early research conducted in the author’s laboratory from 1975 onward stemmed from the even earlier work, beginning in 1964 when the author was a member of the laboratory of Professor Vincent P. Dole at the Rockefeller Institute for Biomedical Research (now the Rockefeller University) (Dole et al. 1966; Kreek 1972, 1973a; Kreek et al. 1972). At that time, scientists were challenged to develop a treatment for opiate dependency, a problem that is still being addressed, but for which there are now three different pharmacotherapeutic approaches approved by the Food and Drug Administration (FDA), and a fourth under investigation. This chapter will review briefly some of the early concepts because they are relevant for the current major problem: developing a new medication (and possibly a variety of medications) for treating cocaine dependency.
作者的实验室从1975年开始进行的早期研究源于更早的工作，始于1964年，当时作者是洛克菲勒生物医学研究所(现为洛克菲勒大学)文森特·p·多尔教授实验室的成员(多尔等人，1966年;克里克1972,1973a;Kreek et al. 1972)。当时，科学家们面临的挑战是开发一种治疗阿片类药物依赖的方法，这个问题仍在解决中，但现在有三种不同的药物治疗方法得到了美国食品和药物管理局(FDA)的批准，第四种正在调查中。本章将简要回顾一些早期的概念，因为它们与当前的主要问题有关:开发一种治疗可卡因依赖的新药(可能还有各种药物)。
{"title":"Goals and rationale for pharmacotherapeutic approach in treating cocaine dependence: insights from basic and clinical research.","authors":"M. Kreek","doi":"10.1037/e495552006-002","DOIUrl":"https://doi.org/10.1037/e495552006-002","url":null,"abstract":"The early research conducted in the author’s laboratory from 1975 onward stemmed from the even earlier work, beginning in 1964 when the author was a member of the laboratory of Professor Vincent P. Dole at the Rockefeller Institute for Biomedical Research (now the Rockefeller University) (Dole et al. 1966; Kreek 1972, 1973a; Kreek et al. 1972). At that time, scientists were challenged to develop a treatment for opiate dependency, a problem that is still being addressed, but for which there are now three different pharmacotherapeutic approaches approved by the Food and Drug Administration (FDA), and a fourth under investigation. This chapter will review briefly some of the early concepts because they are relevant for the current major problem: developing a new medication (and possibly a variety of medications) for treating cocaine dependency.","PeriodicalId":76229,"journal":{"name":"NIDA research monograph","volume":"8 1","pages":"5-35"},"PeriodicalIF":0.0,"publicationDate":"1997-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57798197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Missing data problems have been a thorn in the side of prevention researchers for years. Although some solutions for these problems have been available in the statistical literature, these solutions have not found their way into mainstream prevention research. This chapter is meant to serve as an introduction to the systematic application of the missing data analysis solutions presented recently by Little and Rubin (1987) and others. The chapter does not describe a complete strategy, but it is relevant for (1) missing data analysis with continuous (but not categorical) data, (2) data that are reasonably normally distributed, and (3) solutions for missing data problems for analyses related to the general linear model in particular, analyses that use (or can use) a covariance matrix as input. The examples in the chapter come from drug prevention research. The chapter discusses (1) the problem of wanting to ask respondents more questions than most individuals can answer; (2) the problem of attrition and some solutions; and (3) the problem of special measurement procedures that are too expensive or time consuming to obtain for all subjects. The authors end with several conclusions: Whenever possible, researchers should use the Expectation-Maximization (EM) algorithm (or other maximum likelihood procedure, including the multiple-group structural equation-modeling procedure or, where appropriate, multiple imputation, for analyses involving missing data [the chapter provides concrete examples]); If researchers must use other analyses, they should keep in mind that these others produce biased results and should not be relied upon for final analyses; When data are missing, the appropriate missing data analysis procedures do not generate something out of nothing but do make the most out of the data available; When data are missing, researchers should work hard (especially when planning a study) to find the cause of missingness and include the cause in the analysis models; and Researchers should sample the cases originally missing (whenever possible) and adjust EM algorithm parameter estimates accordingly.
{"title":"Analysis With Missing Data in Prevention Research","authors":"J. Graham, S. Hofer, A. Piccinin","doi":"10.1037/10222-010","DOIUrl":"https://doi.org/10.1037/10222-010","url":null,"abstract":"Missing data problems have been a thorn in the side of prevention researchers for years. Although some solutions for these problems have been available in the statistical literature, these solutions have not found their way into mainstream prevention research. This chapter is meant to serve as an introduction to the systematic application of the missing data analysis solutions presented recently by Little and Rubin (1987) and others. The chapter does not describe a complete strategy, but it is relevant for (1) missing data analysis with continuous (but not categorical) data, (2) data that are reasonably normally distributed, and (3) solutions for missing data problems for analyses related to the general linear model in particular, analyses that use (or can use) a covariance matrix as input. The examples in the chapter come from drug prevention research. The chapter discusses (1) the problem of wanting to ask respondents more questions than most individuals can answer; (2) the problem of attrition and some solutions; and (3) the problem of special measurement procedures that are too expensive or time consuming to obtain for all subjects. The authors end with several conclusions: Whenever possible, researchers should use the Expectation-Maximization (EM) algorithm (or other maximum likelihood procedure, including the multiple-group structural equation-modeling procedure or, where appropriate, multiple imputation, for analyses involving missing data [the chapter provides concrete examples]); If researchers must use other analyses, they should keep in mind that these others produce biased results and should not be relied upon for final analyses; When data are missing, the appropriate missing data analysis procedures do not generate something out of nothing but do make the most out of the data available; When data are missing, researchers should work hard (especially when planning a study) to find the cause of missingness and include the cause in the analysis models; and Researchers should sample the cases originally missing (whenever possible) and adjust EM algorithm parameter estimates accordingly.","PeriodicalId":76229,"journal":{"name":"NIDA research monograph","volume":"142 1","pages":"13-63"},"PeriodicalIF":0.0,"publicationDate":"1997-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57476962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1997-01-01DOI: 10.3109/10826089709035606
M. Krohn, T. Thornberry
No significant differences are found in the density or multiplexity of friendship networks of adolescent drug users and non-users. However, users are more likely than non-users to have friends from the same neighborhood, to have more intimate friendship networks, and to change friends over time. Users are less likely to have friends from the same school and to have parents involved in extracurricular activities. Networks of White users and non-users are more similar than those of Hispanics or African Americans.
{"title":"Network Theory: A Model for Understanding Drug Abuse Among African-American and Hispanic Youth","authors":"M. Krohn, T. Thornberry","doi":"10.3109/10826089709035606","DOIUrl":"https://doi.org/10.3109/10826089709035606","url":null,"abstract":"No significant differences are found in the density or multiplexity of friendship networks of adolescent drug users and non-users. However, users are more likely than non-users to have friends from the same neighborhood, to have more intimate friendship networks, and to change friends over time. Users are less likely to have friends from the same school and to have parents involved in extracurricular activities. Networks of White users and non-users are more similar than those of Hispanics or African Americans.","PeriodicalId":76229,"journal":{"name":"NIDA research monograph","volume":"32 1","pages":"1931-1936"},"PeriodicalIF":0.0,"publicationDate":"1997-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3109/10826089709035606","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69564825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Sayre, D. Engelhart, D. Nadkarni, M. K. Manoj Babu, A. M. Flammang, G. Mccoy
Aliphatic cyclic tertiary amines constitute a major class of naturally occurring and synthetic drugs directed at central biogenic amine receptors. Microsomal metabolism of these amines is known to be associated with low levels of covalent binding and/or suicide inactivation of the pertinent metabolizing P-450 isozymes; two of the more notorious examples are phencyclidine (1-(1phenylcyclohexyl)piperidine) (PCP) (Hoag et al. 1984) and nicotine (Shigenaga et al. 1988).
{"title":"The role of iminium-enamine species in the toxication and detoxication of cyclic tertiary amines.","authors":"L. Sayre, D. Engelhart, D. Nadkarni, M. K. Manoj Babu, A. M. Flammang, G. Mccoy","doi":"10.1037/e495572006-009","DOIUrl":"https://doi.org/10.1037/e495572006-009","url":null,"abstract":"Aliphatic cyclic tertiary amines constitute a major class of naturally occurring and synthetic drugs directed at central biogenic amine receptors. Microsomal metabolism of these amines is known to be associated with low levels of covalent binding and/or suicide inactivation of the pertinent metabolizing P-450 isozymes; two of the more notorious examples are phencyclidine (1-(1phenylcyclohexyl)piperidine) (PCP) (Hoag et al. 1984) and nicotine (Shigenaga et al. 1988).","PeriodicalId":76229,"journal":{"name":"NIDA research monograph","volume":"173 1","pages":"106-27"},"PeriodicalIF":0.0,"publicationDate":"1997-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57799776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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