Evidence Based Mental Health最新文献

{"title":"Using computerised adaptive tests to screen for perinatal depression in underserved women of colour.","authors":"Elizabeth Wenzel,&nbsp;Beatriz Penalver Bernabe,&nbsp;Shannon Dowty,&nbsp;Unnathi Nagelli,&nbsp;Lacey Pezley,&nbsp;Robert Gibbons,&nbsp;Pauline Maki","doi":"10.1136/ebmental-2021-300262","DOIUrl":"https://doi.org/10.1136/ebmental-2021-300262","url":null,"abstract":"<p><strong>Background: </strong>Compared with traditional screening questionnaires, computerised adaptive tests for severity of depression (CAT-DI) and computerised adaptive diagnostic modules for depression (CAD-MDD) show improved precision in screening for major depressive disorder. CAT measures have been tailored to perinatal women but have not been studied in low-income women of colour despite high rates of perinatal depression (PND).</p><p><strong>Objective: </strong>This study aimed to examine the concordance between CAT and traditional measures of depression in a sample of primarily low-income black and Latina women.</p><p><strong>Methods: </strong>In total, 373 women (49% black; 29% Latina) completed the Patient Health Questionnaire-9 (PHQ-9), CAD-MDD and CAT-DI at 845 visits across pregnancy and postpartum. We examined the concordance between continuous CAT-DI and PHQ-9 scores and between binary measures of PND diagnosis on CAD-MDD and the PHQ-9 (cut-off score >10). We examined cases with a positive PND diagnosis on the CAD-MDD but not on the PHQ-9 ('missed' cases) to determine whether clinic notes were consistent with CAD-MDD results.</p><p><strong>Findings: </strong>CAT-DI and PHQ-9 scores were significantly associated (concordance correlation coefficient=0.67; 95% CI 0.58 to 0.74). CAD-MDD detected 5% more case of PND compared with PHQ-9 (p<0.001). The average per-visit rate of PND was 14.4% (14.5% in blacks, 14.9% in Latinas) on the CAD-MDD, and 9.5% (9.8% in blacks, 8.8% in Latinas) on the PHQ-9. Clinical notes were available on 60% of 'missed' cases and validated CAD-MDD PND diagnosis in 89% of cases.</p><p><strong>Conclusions: </strong>CAD-MDD detected 5% more cases of PND in women of colour compared with traditional tests, and the majority of these cases were verified by clinician notes.</p><p><strong>Clinical implications: </strong>Use of CAT in routine clinic care may address health disparities in PND screening.</p>","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":"25 1","pages":"23-28"},"PeriodicalIF":5.2,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8792164/pdf/ebmental-2021-300262.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10293306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dose-effect meta-analysis for psychopharmacological interventions using randomised data.","authors":"Tasnim Hamza,&nbsp;Toshi A Furukawa,&nbsp;Nicola Orsini,&nbsp;Andrea Cipriani,&nbsp;Georgia Salanti","doi":"10.1136/ebmental-2021-300278","DOIUrl":"https://doi.org/10.1136/ebmental-2021-300278","url":null,"abstract":"<p><strong>Objective: </strong>The current practice in meta-analysis of the effects of psychopharmacological interventions ignors the administered dose or restricts the analysis in a dose range. This may introduce unnecessary uncertainty and heterogeneity. Methods have been developed to integrate the dose-effect models in meta-analysis.</p><p><strong>Methods: </strong>We describe the two-stage and the one-stage models to conduct a dose-effect meta-analysis using common or random effects methods. We illustrate the methods on a dataset of selective serotonin reuptake inhibitor antidepressants. The dataset comprises 60 randomised controlled trials. The dose-effect is measured on an odds ratio scale and is modelled using restricted cubic splines to detect departure from linearity.</p><p><strong>Results: </strong>The estimated summary curve indicates that the probability of response increases up to 30 mg/day of fluoxetine-equivalent which results in reaching 50% probability to respond. Beyond 40 mg/day, no further increase in the response is observed. The one-stage model includes all studies, resulting in slightly less uncertainty than the two-stage model where only part of the data is analysed.</p><p><strong>Conclusions: </strong>The dose-effect meta-analysis enables clinicians to understand how the effect of a drug changes as a function of its dose. Such analysis should be conducted in practice using the one-stage model that incorporates evidence from all available studies.</p>","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":"25 1","pages":"1-6"},"PeriodicalIF":5.2,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231575/pdf/ebmental-2021-300278.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10270456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Promoting inclusivity by ensuring that all patients with mental health issues are offered research opportunities in the NHS.","authors":"Catherine Henshall,&nbsp;Helen Jones,&nbsp;Tanya Smith,&nbsp;Andrea Cipriani","doi":"10.1136/ebmental-2021-300411","DOIUrl":"https://doi.org/10.1136/ebmental-2021-300411","url":null,"abstract":"Researchactive clinical services have lower mortality rates and produce higher quality care outcomes, however, recruiting participants to clinical research in the National Health System (NHS) remains challenging. A recent study, assessing the feasibility of clinical staff electronically documenting patient consent to discuss research participation, indicated very low patient uptake, limiting its effectiveness as a strategy for improving access to research. A followon study comparing this ‘optin’ approach with an ‘optout’ approach, whereby patients are informed about research opportunities unless they indicate otherwise, found that patients and staff favoured an ‘optout’ approach and wanted research to be more accessible. Subsequently, in August 2021, Count me In was developed and launched within Oxford Health NHS Foundation Trust adult and older adult mental health services. Count Me In is an optout initiative and a 12month implementation study, aiming to promote inclusivity by enabling greater equity of information provision for marginalised groups (including Black, Asian and minority ethnic groups, older adults, people with physical and mental disabilities, refugees and asylum seekers), rather than relying on clinicianled recruitment. It was developed in consultation with our Caldicott Guardian and Head of Information Governance to ensure correct handling of patient data and to differentiate the initiative from ‘national data optout’. A robust communications plan raised awareness of the initiative (https://www. oxfordhealth.nhs.uk/publication/countme-in/). Patient contact preferences and research involvement are documented on the electronic patient record. Preliminary findings illustrate that in just over 3 months, 8824 patients became contactable through Count Me In, a 400% increase on the number previously contactable through the ‘standard’ optin. Only 120 patients have opted out of contact. Of 234 potentially eligible patients contacted about specific research studies, 46 (19.6%) consented to participate. Inclusivity across age, gender, ethnicity and diagnostic group is being monitored and early evidence signals positive changes in equity of research access. For instance, the Count Me In cohort now represents patients across 62 of the 70 diagnostic groups represented in the Trust’s caseload, in comparison to only 44 groups represented using the standard ‘optin’ approach. A full evaluation at the end of the 12month implementation phase will highlight trends and changes in research activity, while also allowing for process modifications to be made before the initiative is rolled out across the country. The ultimate aim is to extend Count Me In to as many NHS Trusts as possible, embedding research within routine patient care and promoting inclusivity by ensuring that research opportunities are offered to all patients with mental health issues, regardless of diagnosis or how well known they are to clinicians.","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":"25 1","pages":"e1"},"PeriodicalIF":5.2,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231587/pdf/ebmental-2021-300411.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10582051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prevalences of comorbid anxiety disorder and daily smartphone-based self-reported anxiety in patients with newly diagnosed bipolar disorder.","authors":"Sharleny Stanislaus,&nbsp;Klara Coello,&nbsp;Hanne Lie Kjærstad,&nbsp;Kimie Stefanie Ormstrup Sletved,&nbsp;Ida Seeberg,&nbsp;Mads Frost,&nbsp;Jakob Eyvind Bardram,&nbsp;Rasmus Nejst Jensen,&nbsp;Maj Vinberg,&nbsp;Maria Faurholt-Jepsen,&nbsp;Lars Vedel Kessing","doi":"10.1136/ebmental-2021-300259","DOIUrl":"https://doi.org/10.1136/ebmental-2021-300259","url":null,"abstract":"<p><strong>Background: </strong>Around 40% of patients with bipolar disorder (BD) additionally have anxiety disorder. The prevalence of anxiety in patients with newly diagnosed BD and their first-degree relatives (UR) has not been investigated.ObjectiveTo investigate (1) the prevalence of a comorbid anxiety diagnosis in patients with newly diagnosed BD and their UR, (2) sociodemographic and clinical differences between patients with and without a comorbid anxiety diagnosis and (3) the association between smartphone-based patient-reported anxiety and observer-based ratings of anxiety and functioning, respectively.</p><p><strong>Methods: </strong>We recruited 372 patients with BD and 116 of their UR. Daily smartphone-based data were provided from 125 patients. SCAN was used to assess comorbid anxiety diagnoses.</p><p><strong>Findings: </strong>In patients with BD, the prevalence of a comorbid anxiety disorder was 11.3% (N=42) and 10.3% and 5.9% in partial and full remission, respectively. In UR, the prevalence was 6.9%. Patients with a comorbid anxiety disorder had longer illness duration (p=0.016) and higher number of affective episodes (p=0.011). Smartphone-based patient-reported anxiety symptoms were associated with ratings of anxiety and impaired functioning (p<0.001).</p><p><strong>Limitations: </strong>The SCAN interviews to diagnose comorbid anxiety disorder were carried out regardless of the participants' mood state.Clinical implicationsThe lower prevalence of anxiety in newly diagnosed BD than in later stages of BD indicates that anxiety increases with progression of BD. Comorbid anxiety seems associated with poorer clinical outcomes and functioning and smartphones are clinically useful for monitoring anxiety symptoms.</p><p><strong>Trial registration number: </strong>ClinicalTrials.gov Registry (NCT02888262).</p>","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":"24 4","pages":"137-144"},"PeriodicalIF":5.2,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231557/pdf/ebmental-2021-300259.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10214193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guidelines to understand and compute the number needed to treat.","authors":"Valentin Vancak,&nbsp;Yair Goldberg,&nbsp;Stephen Z Levine","doi":"10.1136/ebmental-2020-300232","DOIUrl":"https://doi.org/10.1136/ebmental-2020-300232","url":null,"abstract":"<p><strong>Objective: </strong>We aim to explain the unadjusted, adjusted and marginal number needed to treat (NNT) and provide software for clinicians to compute them.</p><p><strong>Methods: </strong>The NNT is an efficacy index that is commonly used in randomised clinical trials. The NNT is the average number of patients needed to treat to obtain one successful outcome (ie, response) due to treatment. We developed the nntcalc R package for desktop use and extended it to a user-friendly web application. We provided users with a user-friendly step-by-step guide. The application calculates the NNT for various models with and without explanatory variables. The implemented models for the adjusted NNT are linear regression and analysis of variance (ANOVA), logistic regression, Kaplan-Meier and Cox regression. If no explanatory variables are available, one can compute the unadjusted Laupacis <i>et al</i>'s NNT, Kraemer and Kupfer's NNT and the Furukawa and Leucht's NNT. All NNT estimators are computed with their associated appropriate 95% confidence intervals. All calculations are in R and are replicable.</p><p><strong>Results: </strong>The application provides the user with an easy-to-use web application to compute the NNT in different settings and models. We illustrate the use of the application from examples in schizophrenia research based on the Positive and Negative Syndrome Scale. The application is available from https://nntcalc.iem.technion.ac.il. The output is given in a journal compatible text format, which users can copy and paste or download in a comma-separated values format.</p><p><strong>Conclusion: </strong>This application will help researchers and clinicians assess the efficacy of treatment and consequently improve the quality and accuracy of decisions.</p>","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":"24 4","pages":"131-136"},"PeriodicalIF":5.2,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231569/pdf/ebmental-2020-300232.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10212217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative efficacy and acceptability of psychotherapies for post-traumatic stress disorder in children and adolescents: a systematic review and network meta-analysis.","authors":"Yajie Xiang,&nbsp;Andrea Cipriani,&nbsp;Teng Teng,&nbsp;Cinzia Del Giovane,&nbsp;Yuqing Zhang,&nbsp;John R Weisz,&nbsp;Xuemei Li,&nbsp;Pim Cuijpers,&nbsp;Xueer Liu,&nbsp;Jürgen Barth,&nbsp;Yuanliang Jiang,&nbsp;David Cohen,&nbsp;Li Fan,&nbsp;Donna Gillies,&nbsp;Kang Du,&nbsp;Arun V Ravindran,&nbsp;Xinyu Zhou,&nbsp;Peng Xie","doi":"10.1136/ebmental-2021-300346","DOIUrl":"https://doi.org/10.1136/ebmental-2021-300346","url":null,"abstract":"<p><strong>Background: </strong>Available evidence on the comparative efficacy and acceptability of psychotherapies for post-traumatic stress disorder (PTSD) in children and adolescents remains uncertain.</p><p><strong>Objective: </strong>We aimed to compare and rank the different types and formats of psychotherapies for PTSD in children and adolescents.</p><p><strong>Methods: </strong>We searched eight databases and other international registers up to 31 December 2020. The pairwise meta-analyses and frequentist network meta-analyses estimated pooled standardised mean differences (SMDs) and ORs with random-effects model. Efficacy at post-treatment and follow-up, acceptability, depressive and anxiety symptoms were measured.</p><p><strong>Findings: </strong>We included 56 randomised controlled trials with 5327 patients comparing 14 different types of psychotherapies and 3 control conditions. For efficacy, cognitive processing therapy (CPT), behavioural therapy (BT), individual trauma-focused cognitive-behavioural therapy (TF-CBT), eye movement desensitisation and reprocessing, and group TF-CBT were significantly superior to all control conditions at post-treatment and follow-up (SMDs between -2.42 and -0.25). Moreover, CPT, BT and individual TF-CBT were more effective than supportive therapy (SMDs between -1.92 and -0.49). Results for depressive and anxiety symptoms were similar to the findings for the primary outcome. Most of the results were rated as 'moderate' to 'very low' in terms of confidence of evidence.</p><p><strong>Conclusions: </strong>CPT, BT and individual TF-CBT appear to be the best choices of psychotherapy for PTSD in young patients. Other types and different ways of delivering psychological treatment can be alternative options. Clinicians should consider the importance of each outcome and the patients' preferences in real clinical practice.</p>","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":"24 4","pages":"153-160"},"PeriodicalIF":5.2,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/0c/75/ebmental-2021-300346.PMC8543231.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10269967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correspondence on \"How can we estimate QALYs based on PHQ-9 scores? Equipercentile linking analysis of PHQ-9 and EQ-5D\" by Furukawa <i>et al</i>.","authors":"Matthew Franklin,&nbsp;Tracey Young","doi":"10.1136/ebmental-2021-300265","DOIUrl":"https://doi.org/10.1136/ebmental-2021-300265","url":null,"abstract":"Furukawa et al posed the question: how can we estimate qualityadjusted life years (QALYs) based on Patient Health Questionnaire-9 (PHQ-9) scores? They recommend equipercentile linking analysis between the depression severity PHQ-9 and preferencebased EQ5D threelevel version (EQ5D3L; UK value set), the latter used to estimate utility data for QALYs. Furukawa et al refer to the process of ‘crosswalking’, whereby the practice of fitting a statistical model to health utility data has been referred to as ‘mapping’ and 'crosswalking’. Furukawa et al reference two mappingrelated papers (their references 7 and 9); however, their analysis seems to have missed rigorous mapping methodology and previous studies which have used these mapping processes, alongside other conceptual considerations when wanting to ‘crosswalk’/‘map’ from a nonpreferencebased (often conditionspecific) measure such as the PHQ-9 to the preferencebased EQ5D3L. Clear guidance for mapping has been set out by Wailoo et al. A case for equipercentile linking for mapping has been made based on suggested limitations of the more commonly used regression methods; the case for regression is described by Alava et al. A systematic review of mapping studies published in 2019 states: ‘There were 180 papers with 233 mapping functions in total [identified]...The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with [...] greater reporting of predictive ability of mapping functions’. From this review, the majority of mapping functions were generated to obtain EQ5D3L/EQ5D fivelevel version (EQ5D5L)/childfriendly EQ5D version (EQ5DY) scores (n=147) among other preferencebased measure scores; eg, ShortForm SixDimension (SF6D, n=45). Furukawa et al reference one study, which was also identified by Mukuria et al, which maps from the PHQ-9 to the SF6D (not EQ5D3L), which concluded that: ‘mapping from mental health conditionspecific measures, such as the widely used PHQ-9, GAD [(Generalized Anxiety Disorder)] and HADS [(Hospital Anxiety and Depression Scale)], may not be an appropriate approach to generating EQ5D and SF6D scores as these measures focus on specific symptoms and not on the wider impact of mental health conditions’ (their reference 7). Furukawa et al is mapping and therefore existing rigorous mapping methods should be used and compared with the suggested equipercentile linking analysis. We recommend not using the suggested conversion table by Furukawa et al until further conceptual and statistical analyses have been conducted, including reporting of performance statistics to allow method performance to be judged and compared against existing mapping studies in the empirical literature. We make this recommendation on the basis that Furukawa et al currently provides no reported performance statistics or comparisons to suggest the potential predictive ability of using the conversion table; therefore there is no way to judge t","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":"24 4","pages":"e5"},"PeriodicalIF":5.2,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1136/ebmental-2021-300265","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10565607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brief Evidence-Based Interventions for Child and Adolescent Mental Health","authors":"P. Lusk, B. Melnyk","doi":"10.1891/9780826167279.0017","DOIUrl":"https://doi.org/10.1891/9780826167279.0017","url":null,"abstract":"","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":"50 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76022265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reimbursement for Mental/Behavioral Health Services in Primary Care","authors":"N. Herendeen","doi":"10.1891/9780826167279.0016","DOIUrl":"https://doi.org/10.1891/9780826167279.0016","url":null,"abstract":"","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":"1 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72968785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Helping Children and Parents Through Marital Separation and Divorce","authors":"","doi":"10.1891/9780826167279.0010","DOIUrl":"https://doi.org/10.1891/9780826167279.0010","url":null,"abstract":"","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":"22 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74842348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}