Drug Safety最新文献

{"title":"Charting and Sidestepping the Pitfalls of Disproportionality Analysis.","authors":"Michele Fusaroli, Daniele Sartori, Eugène P van Puijenbroek, G Niklas Norén","doi":"10.1007/s40264-025-01604-y","DOIUrl":"10.1007/s40264-025-01604-y","url":null,"abstract":"<p><p>Disproportionality analysis is used by many pharmacovigilance organizations for detecting and assessing signals of potential adverse drug reactions. However, its goal is often misunderstood and the approach misapplied, leading to erroneous conclusions due to neglected violated assumptions. In this paper we illustrate how simplistic use and interpretation of disproportionality analysis can lead to incorrect conclusions. Using VigiBase, the WHO global database of adverse event reports, and the Information Component disproportionality metric, we provide selected examples to highlight common sources of error that can introduce spurious disproportionalities or lead to missing important signals: confounding (by age, sex, indication, comedication), effect modification (by age), notoriety bias, masking, misclassification (by miscoding, incomplete or imprecise event retrieval), neglecting report utility, and violated independence assumption. Additionally, we present how sophisticated analyses may introduce new biases or amplify existing ones, such as collider bias or masking amplification. Due to its pitfalls, disproportionality analysis plays a supportive rather than decisive role in signal detection and assessment. Careful design and interpretation of disproportionality analysis, with appropriate subgrouping and clinical assessment, are essential. While subgrouping can mitigate some pitfalls, it reduces sample size and may introduce or amplify existing biases and needs to be used with care. Further development of tools to detect and mitigate biases in disproportionality analyses, and to assess their risk of bias, is needed.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136958","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":"Implied ADR-Admissions: A Cohort Study Introducing a Novel Administrative Data Approach for Identifying Drug-Related Hospitalisations.","authors":"Miriam Schechner, Marietta Rottenkolber, Clara Weglage, Vita Brišnik, Annette Haerdtlein, Bruce Guthrie, Ulrich Jaehde, Eva Grill, Tobias Dreischulte","doi":"10.1007/s40264-025-01614-w","DOIUrl":"https://doi.org/10.1007/s40264-025-01614-w","url":null,"abstract":"<p><strong>Background: </strong>Adverse drug reactions (ADRs) are a key contributor to unplanned hospitalisations, particularly in patients with polypharmacy. Traditional detection methods, such as expert reviews or diagnostic coding, are limited in scalability and sensitivity.</p><p><strong>Objective: </strong>This study introduces and evaluates a novel scalable method, implied ADR-admissions, that links drug exposures to adverse events using administrative data to improve the detection of plausible drug-related hospitalisations.</p><p><strong>Methods: </strong>A retrospective cohort study was conducted using linked health data from 123,662 individuals aged ≥ 40 years with polypharmacy in two Scottish health boards. Implied ADR-admissions were defined as emergency hospitalisations with one of 15 adverse events plausibly linked to drug exposure (based on a structured consensus process) within the prior 90 days. Incidence was compared with three existing approaches: adverse event-admissions (regardless of drug exposure), explicit ADR-admissions (explicitly coded as ADRs) and preventable ADR-admissions (with prior medication error). Multivariate logistic regression was used to identify predictors of implied ADR-admissions.</p><p><strong>Results: </strong>Over 1 year, 2.6% experienced an implied ADR-admission, compared with 5.7% with adverse event-admissions, and 0.4% with explicit ADR-admissions. For gastrointestinal bleeding, the implied ADR-admission incidence was 20 times higher than the preventable ADR-admission incidence. Key predictors for implied ADR-admissions included prior hypokalaemia-related hospitalisation and use of potentially inappropriate medications.</p><p><strong>Conclusions: </strong>The implied ADR-admission approach has improved specificity relative to broad adverse event definitions while enhancing sensitivity beyond methods that rely solely on explicit ADR codes or pre-specified medication errors. It offers a scalable automated tool for pharmacovigilance, though further validation is needed prior to routine use in medication safety monitoring.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069263","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":"Characterizing the FDA Adverse Event Reporting System (FAERS) as a Network to Improve Pattern Discovery.","authors":"Raechel Davis, Oanh Dang, Suranjan De, Robert Ball","doi":"10.1007/s40264-025-01609-7","DOIUrl":"https://doi.org/10.1007/s40264-025-01609-7","url":null,"abstract":"<p><strong>Introduction: </strong>In drug-safety monitoring systems, adverse events (AEs) associated with the use of medical products often consist of complex patterns of clinical events. Network analysis (NA) was used for pattern recognition and characterizing the Vaccine Adverse Event Reporting System (VAERS), but limited applications of NA to the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) left its network description incomplete.</p><p><strong>Methods: </strong>In this analysis, the network properties of FAERS were characterized and leveraged to facilitate pattern discovery. Reported AE information in FAERS is represented using preferred terms (PTs) in Medical Dictionary for Regulatory Activities terminology. The FAERS subsets were analyzed with drugs and PTs as nodes and interconnections as edges. Global characteristics, like the scale-free nature of the distribution, were examined to explore theoretical and structural considerations. Metrics that assess connectivity and edge weighting algorithms based on report co-occurrence or clustering were applied.</p><p><strong>Results: </strong>Serious AE reports from 2016 to 2023 (2,062,099) were represented as a network of 20,965 nodes (16,847 PTs and 4116 drugs) with more than four million interconnections. Characteristics of FAERS subnetworks were determined with heavy-tailed degree distributions, high local clustering, and low diameters. Complexities related to structural and evolutionary characteristics were revealed as the log-normal model fits the degree distribution better than the power law.</p><p><strong>Conclusions: </strong>Network-based techniques identified clinically relevant patterns and clustering patterns representative of known adverse drug reactions. Comparisons to VAERS reveal similarities in networks of AE reporting systems. This initial systematic application of NA to FAERS describes the overall network characteristics of the FAERS database and provides insight into the use of network applications in drug safety research.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069248","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":"Managing Drug Interactions with Enzalutamide in Patients with Prostate Cancer: A Podcast.","authors":"Alicia K Morgans, Brooke Looney, Jesse Mack, Judeth Bianco","doi":"10.1007/s40264-025-01600-2","DOIUrl":"https://doi.org/10.1007/s40264-025-01600-2","url":null,"abstract":"<p><p>Enzalutamide is an oral androgen receptor signaling inhibitor used in the treatment of prostate cancer. Elderly patients with prostate cancer commonly have age-related comorbidities that require concomitant, active treatment. As a moderate inducer of the cytochrome P450 (CYP) enzymes CYP2C9 and CYP2C19, and a strong inducer of CYP3A4, there is potential for drug-drug interactions (DDIs) when enzalutamide is coadministered with other drugs that are CYP3A4 substrates-resulting in loss of efficacy or increased risk of unintended drug-related adverse events. In this podcast, we describe enzalutamide including its dosing, pharmacokinetics, and potential for interaction with coadministered drugs using several hypothetical patient cases with real-world clinical implications. Discussion of each patient case will highlight management strategies and illustrate that nearly all enzalutamide drug-drug interactions can be effectively managed with appropriate knowledge of which drugs pose interaction risks, when dose adjustments are indicated, and when alternative drugs can be substituted. Supplementary file1 (MP4 192541 KB).</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069260","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":"The Development and Use of Office of New Drugs Custom Medical Queries for Safety Analyses of Clinical Trial Data.","authors":"Scott Proestel, Vaishali Popat, Ellis F Unger, Linda J B Jeng","doi":"10.1007/s40264-025-01582-1","DOIUrl":"https://doi.org/10.1007/s40264-025-01582-1","url":null,"abstract":"<p><p>The evaluation of safety data by the US Food and Drug Administration (FDA) is a critical step in the review of marketing applications for drugs and biologics. It can be difficult to identify a safety signal, and important signals can be missed if not evaluated comprehensively. Adverse events reported by study participants constitute a major source of safety data, and while previously established standard term groupings have been useful for analysis (e.g., Standardised MedDRA^® Queries), the Office of New Drugs (OND) at the FDA determined a need for more clinically meaningful groupings specifically designed for use in premarket drug safety evaluation. To improve safety signal detection and analyses of adverse reactions, OND developed standard groupings of adverse event terms known as OND Custom Medical Queries (OCMQs). OCMQs are intended to capture clinically meaningful groupings (i.e., safety signals) represented in premarketing data. OND has seen great utility in OCMQs during premarket drug safety evaluations, as they have improved OND's ability to detect safety signals and to distinguish and quantify adverse reactions in clinical trial data.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145063831","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":"Algorithms to Identify Major Congenital Malformations in Routinely Collected Healthcare Data: A Systematic Review.","authors":"Melanie H Jacobson, Meritxell Sabidó, Ana Sofia Afonso, Adebola Ajao, Eman A Alghamdi, Susan E Andrade, Dimitri Bennett, Vineetkumar Kharat, Marie-Laure Kürzinger, Maryline Le Noan-Lainé, Ditte Mølgaard-Nielsen, Gayle Murray, Elena Rivero-Ferrer, Sandra Lopez-Leon","doi":"10.1007/s40264-025-01606-w","DOIUrl":"https://doi.org/10.1007/s40264-025-01606-w","url":null,"abstract":"<p><strong>Introduction: </strong>Major congenital malformations (MCMs) are a primary outcome of interest in pregnancy safety studies.</p><p><strong>Objective: </strong>This study aimed to identify and summarize algorithms used to identify MCMs in routinely collected healthcare data sources in the USA, Canada, and Europe by conducting a systematic literature review.</p><p><strong>Methods: </strong>We developed a search strategy to identify studies containing algorithms for MCMs from January 1, 2010, to April 11, 2025. Search terms included those related to MCMs as an outcome, routinely collected healthcare data, epidemiologic designs likely to incorporate algorithms, and pregnant individuals and/or infants. Study review and data extraction was conducted in duplicate using a standardized data collection form.</p><p><strong>Results: </strong>Among the initially identified 2242 studies, 974 were selected for full-text review. Of these, 70.3% were excluded, leaving 289 studies. Over half (58.1%) of the included studies were from Europe, predominantly from Nordic countries using national register data (N = 135; 80.4%). Studies using claims (18.0%) or hospital discharge data (16.3%) were also common. Although there was heterogeneity in the timing of MCM assessment, 55.7% of studies collected MCMs through the infant's first year of life. Overall, algorithms varied across data source type and geography in the codes specified, rules, utilization of maternal versus infant records, and coding system. There were 27 (9.3%) validation studies, 70.4% of which were based on claims and/or electronic health record data only. Most had positive predictive values >70%, though this varied according to MCM type or anatomical site.</p><p><strong>Conclusion: </strong>We provide the first comprehensive systematic literature review of algorithms used to identify MCMs in routinely collected healthcare data, aiding researchers in their ability to generate reliable evidence in pregnancy safety pharmacoepidemiology.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145052430","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":"Comparative Risk of Acute Kidney Injury with Piperacillin-Tazobactam Plus Teicoplanin Versus Piperacillin-Tazobactam Plus Vancomycin: A Systematic Review and Meta-Analysis.","authors":"Shahd Mohammad, Haneen Ghazal, Wafaa Rahimeh, Maqsood Khan, Mosab Al Balas, Faris El-Dahiyat","doi":"10.1007/s40264-025-01611-z","DOIUrl":"https://doi.org/10.1007/s40264-025-01611-z","url":null,"abstract":"<p><strong>Background: </strong>Piperacillin-tazobactam combined with vancomycin is widely employed for broad-spectrum empiric coverage but has been increasingly associated with acute kidney injury (AKI). The comparative renal safety of substituting vancomycin with teicoplanin remains uncertain.</p><p><strong>Objective: </strong>This meta-analysis aimed to evaluate renal outcomes between piperacillin-tazobactam plus teicoplanin (TZP-TEI) versus piperacillin-tazobactam plus vancomycin (TZP-VAN).</p><p><strong>Methods: </strong>PubMed, Scopus, and Cochrane Central were searched for studies comparing TZP-TEI versus TZP-VAN in hospitalized patients. The primary outcome was AKI incidence, defined by Kidney disease: Improving global outcomes (KDIGO) or RIFLE (Risk of renal dysfunction, Injury to kidney, Failure or Loss of kidney function, and End-stage kidney disease) criteria. Data were analyzed using Review Manager, with heterogeneity assessed via the I² statistic.</p><p><strong>Results: </strong>A total of 908 patients were included from five cohort studies, four of which applied propensity-score matching (PSM), with reported ages ranging from 56.8 to 79 years. The TZP-TEI regimen was associated with a significantly reduced rate of AKI compared with TZP-VAN (odds ratio [OR] 0.52; 95% confidence interval [CI] 0.30-0.89; p = 0.02; I² = 51%). No statistically significant differences were observed between groups for AKI recovery (OR 0.68; 95% CI 0.41-1.12; p = 0.13; I² = 0%) or for 30-day all-cause mortality (OR 1.34; 95% CI 0.77-2.32; p = 0.30; I² = 0%). Subgroup analyses stratified by AKI severity (KDIGO stages 1-3 or RIFLE criteria) demonstrated consistent directionality across stages, with no significant differences observed within PSM or non-PSM cohorts.</p><p><strong>Conclusion: </strong>The TZP-TEI combination was associated with a significantly lower incidence of AKI than was TZP-VAN. Further studies are warranted to validate these findings, optimize teicoplanin dosing within the TZP-TEI combination, and inform therapeutic drug monitoring implementation in high-risk hospitalized patients.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145052440","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":"Drugs Withdrawn from the Canadian Market for Safety and Effectiveness Reasons, 1990-2024: A Cross-Sectional Study.","authors":"Joel Lexchin","doi":"10.1007/s40264-025-01612-y","DOIUrl":"10.1007/s40264-025-01612-y","url":null,"abstract":"<p><strong>Introduction: </strong>At times it is necessary to withdraw drugs after they have been approved because of lack of effectiveness or safety concerns. Health Canada does not keep a list of withdrawn drugs.</p><p><strong>Objective: </strong>The aim of this study was to generate a list of all drugs approved since 1990 and subsequently withdrawn from the Canadian market for safety or effectiveness reasons until the end of 2024. This list was used to examine trends in the number of withdrawals and the percent of new drugs that are approved but eventually withdrawn.</p><p><strong>Methods: </strong>A list of withdrawn drugs was developed based on previous published research and supplemented by examining lists of withdrawn drugs in other jurisdictions. The time, in years, was calculated between the date of approval and withdrawal. The reasons for withdrawal came from either Health Canada documents or, if unavailable, from international sources. Withdrawals for commercial reasons were not included in the analysis.</p><p><strong>Results: </strong>Of the 1094 drugs approved from January 1, 1990, to December 31, 2024, a total of 37 were withdrawn: 32 were new active substances (molecules never marketed before in any form) and five were other types of new drugs. The median time to withdrawal was 3.60 years (interquartile range 2.45-9.50). Approximately 5% of all new active substances approved in a 5-year period were eventually withdrawn over the period 1990-2009. Between 2010 and 2019, the withdrawal rate was < 2%. The most common reasons for withdrawal were cardiac and liver complications.</p><p><strong>Conclusion: </strong>As a percent of all drugs approved, relatively few drugs are withdrawn, and the number of drug withdrawals as a percent of approvals declined between 2010 and 2019.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145033069","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":"The Promise and Challenge of Large Language Models for Pharmacovigilance.","authors":"Lynette Hirschman","doi":"10.1007/s40264-025-01608-8","DOIUrl":"https://doi.org/10.1007/s40264-025-01608-8","url":null,"abstract":"","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145023103","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":"Characteristics and Risk Factors of Medication Incidents Across Stages of Medication Management in Residential Aged Care: A Longitudinal Cohort Study of 5700 Reported Incidents.","authors":"S Sandun M Silva, Nasir Wabe, Magdalena Z Raban, Amy D Nguyen, Guogui Huang, Ying Xu, Crisostomo Mercado, Desiree C Firempong, Johanna I Westbrook","doi":"10.1007/s40264-025-01602-0","DOIUrl":"https://doi.org/10.1007/s40264-025-01602-0","url":null,"abstract":"<p><strong>Background: </strong>Problems with medication management are consistently identified as key concerns for the quality of residential aged care (RAC). Incident reports can provide valuable information on key issues related to medication management; however, few studies have explored medication incidents in RAC settings.</p><p><strong>Objectives: </strong>To investigate the characteristics of medication incidents at different stages of medication management and identify the risk factors associated with incidents.</p><p><strong>Methods: </strong>A retrospective longitudinal cohort study was conducted using medication incidence data from 25 RAC facilities in New South Wales, Australia. All medication incidents between 1 July 2014 and 31 August 2021 relating to 5709 aged care residents aged ≥ 65 years were included. The outcome measure was the medication incidence rate (IR), quantified as the number of medication incidents per 1000 resident days. A multilevel Poisson regression model was performed to identify risk factors associated with exposure to medication incidents.</p><p><strong>Results: </strong>A total of 5708 medication incidents were analysed. The overall medication IR was 1.81 per 1000 resident days (95% CI 1.76, 1.86). Of 5709 residents, 35% (n = 2016) had at least one recorded medication incident, of which 1095 (> 50%) had more than one. The majority of the incidents were associated with medication administration (3023 incidents, 53%), followed by supply (n = 1546, 27%) and monitoring the response to the medication (n = 548, 9.6%). The outcome of the incident on residents was reported in 5165 (90%) incidents, with 724 (14%) requiring the resident to be monitored by the hospital, general practitioner (GP), or staff. Respite admissions were associated with a higher risk of medication incidents including potentially harmful incidents, compared with permanent admissions (rate ratio (RR) = 1.908, 95% CI 1.646, 2.211, p < 0.01). Residents with Parkinson's disease had a 1.5-fold greater risk of a medication incident (RR = 1.586, 95% CI 1.318, 1.908) compared with residents without Parkinson's. The administration of more than five medications (polypharmacy) was associated with an increased risk of medication incidents (RR = 2.019, 95% CI 1.930, 2.111).</p><p><strong>Conclusions: </strong>Medication incidents affected more than one-third of older adults in RAC facilities. Improvement strategies should focus on medication administration, supply and monitoring, with particular attention given to respite residents and those with multimorbidity and polypharmacy.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006028","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}