Journal of Clinical Epidemiology最新文献

{"title":"Corrigendum to 'Avoiding searching for outcomes called for additional search strategies: a study of cochrane review searches' [Journal of Clinical Epidemiology, 149 (2022) 83-88].","authors":"Tove Faber Frandsen, Michael Friberg Bruun Nielsen, Mette Brandt Eriksen","doi":"10.1016/j.jclinepi.2024.111604","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2024.111604","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111604"},"PeriodicalIF":7.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677477","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":"A methodological review identified several options for utilizing registries for randomized controlled trials.","authors":"Luisa Urban, Nina Haller, Dawid Pieper, Tim Mathes","doi":"10.1016/j.jclinepi.2024.111614","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2024.111614","url":null,"abstract":"<p><strong>Objective: </strong>Registry-based Randomized Controlled Trials (RRCTs) can provide internally valid results in a real-world context at relatively low effort and cost. However, the main characteristics, the extent to which the registry is utilized (e.g., proportion of data from registry), and registry-related limitations are not well characterized. This methodological review of RRCTs aims to analyze the trial design features, investigate potential usage options, and identify possible limitations of using registry data for RCTs.</p><p><strong>Study design and setting: </strong>A systematic search in PubMed for ongoing and published RRCTs was conducted up to 2023/28/02. Studies that reported at least one outcome derived from a registry were included. Study selection was independently performed by two reviewers. All data were extracted into a standardized table, and descriptive statistics were generated.</p><p><strong>Results: </strong>We included 162 RRCTs (41 protocols and 121 studies). Most RRCTs were multicenter trials (n=127; 78.4%) comprising a large number of participants (median=1,787; range=41-683,927) and a long follow-up period (median=60 months; range=1-367 months) with a minimal loss to follow-up. The inclusion criteria of participants were mostly broadly defined. Types of interventions ranged from surgical procedures to behavioral interventions and almost half of the interventions (46.9%) had a preventive purpose. The main registry outcome was mostly a clinical endpoint (40.1%) or a composite endpoint of major clinical events (30.9%) that was objectively measurable. We found different degrees of registry utilization, ranging from the exclusive use of long-term monitoring of previously published data to the more comprehensive registry utilization for patient recruitment, endpoint collection, and long-term follow-up. Limitations related to the use of registry data comprised potential coding errors or incomplete data (e.g., due to under-recording of mild cases). In addition, technical challenges must be considered (e.g., failed linkages or time-delayed data entry).</p><p><strong>Conclusions: </strong>A broad spectrum of potential usage options and usage extent of registry data exist. Our analysis suggests that in many cases, the potential of using registry data and thus their benefits were not fully utilized. In addition, the study illustrates that there is not a single, unified methodology for designing RRCTs but that registries can support RCTs in various ways. Therefore, future RRCTs should specify for what purposes and to what extent registries were utilized. Moreover, a clear definition and taxonomy of RRCTs appears necessary for facilitating future dialogue and research on RRCTs.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111614"},"PeriodicalIF":7.3,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677474","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":"Real-time Adaptive Randomization of Clinical Trials.","authors":"Gui Liberali, Eric Boersma, Hester Lingsma, Jasper Brugts, Diederik Dippel, Jan Tijssen, John Hauser","doi":"10.1016/j.jclinepi.2024.111612","DOIUrl":"10.1016/j.jclinepi.2024.111612","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate real-time (day-to-day) adaptation of randomized controlled clinical trials (RCTs) with delayed endpoints - a \"forward-looking optimal-experimentation\" form of response-adaptive randomization (RAR). To identify the implied tradeoffs between lowered mortality, confidence intervals, statistical power, potential arm misidentification, and endpoint-rate change during the trial.</p><p><strong>Study design and setting: </strong>Using data from RCTs in acute myocardial infarction (30,732 patients in GUSTO-1) and coronary heart disease (12,218 patients in EUROPA), we resample treatment-arm assignments and expected endpoints to simulate (1) real-time assignment, (2) forward-looking assignments adapted after observing a fixed number of patients (\"blocks\"), and (3) a variant that balances RCT and real-time assignments. Blinded RTARs adjust day-to-day arm assignments by optimizing the tradeoff between assigning the (likely) best treatment and learning about endpoint rates for future assignments.</p><p><strong>Results: </strong>Despite delays in endpoints, real-time assignment quickly learns which arm is superior. In the simulations, by the end of the trials, real-time assignment allocated more patients to the superior arm and fewer patients to the inferior arm(s) resulting in fewer mortalities over the course of the trial. Endpoint rates and odds ratios were well within (resampling) confidence intervals of the RCTs, but with tighter confidence intervals on the superior arm and less-tight confidence intervals on the inferior arm(s) and the odds ratios. The variant and patient-block-based adaptation each provide intermediate levels of benefits and costs. When endpoint rates change within a trial, real-time assignment improves estimation of the end-of-trial superior-arm endpoint rates, but exaggerates differences relative to inferior arms. Unlike most RARs, real-time assignment automatically adjusts to reduce biases when real changes are larger.</p><p><strong>Conclusion: </strong>Real-time assignment improves patient outcomes within the trial and narrows the confidence interval for the superior arm. Benefits are balanced with wider confidence intervals on inferior arms and odds ratios. Forward-looking variants provide intermediate benefits and costs. In no simulations, was an inferior arm identified as statistically superior.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111612"},"PeriodicalIF":7.3,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669987","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":"Some superiority trials with non-significant results published in high impact factor journals correspond to non-inferiority situations: a research-on-research study.","authors":"Deivanes Rajendrabose, Lucie Collet, Camille Reinaud, Maxime Beydon, Xiaojun Jiang, Sahra Hmissi, Antonin Vermillac, Thomas Degonzague, David Hajage, Agnès Dechartres","doi":"10.1016/j.jclinepi.2024.111613","DOIUrl":"10.1016/j.jclinepi.2024.111613","url":null,"abstract":"<p><strong>Objective: </strong>Many negative randomized controlled trials (RCTs) report spin in their conclusions to highlight the benefits of the experimental arm, which could correspond to a non-inferiority (NI) objective. We aimed to evaluate whether some negative superiority RCTs comparing two active interventions could correspond to an NI situation and to explore associated trial characteristics.</p><p><strong>Study design and setting: </strong>We searched PubMed for superiority RCTs comparing two active interventions with non-statistically significant results for the primary outcome that were published in 2021 in the 5 journals with the highest impact factor in each medical specialty. Three reviewers independently evaluated whether trials could correspond to an NI situation (i.e., an evaluation of efficacy as the primary outcome, with the experimental intervention presenting advantages including better safety profile, ease of administration, or decreased cost as compared with the control intervention).</p><p><strong>Results: </strong>Of the 147 trials included, 19 (12.9%, 95% CI [7.9%, 19.4%]) corresponded to a potential NI situation, and as compared with trials not in a potential NI situation, they were published in a journal with a lower impact factor (median impact factor 8.7 vs 15.6), were more frequently rated at high or some concerns regarding risk of bias (n=14, 73.7% vs. n=69, 53.9%) and reported spin in the article conclusions (n=11, 57.9% vs. n=24, 18.8%).</p><p><strong>Conclusion: </strong>A non-negligible proportion of superiority negative trials comparing two active interventions could correspond to an NI situation. These trials seemed at increased risk of bias and frequently reported spin in the conclusions, which may distort the interpretation of results.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111613"},"PeriodicalIF":7.3,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669988","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":"Directed acyclic graph helps to understand the causality of malnutrition in under-five children born small for gestational age.","authors":"Soumya Tiwari, Viswas Chhapola, Nisha Chaudhary, Lokesh Sharma","doi":"10.1016/j.jclinepi.2024.111611","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2024.111611","url":null,"abstract":"<p><strong>Objectives: </strong>Small-for-gestational age (SGA) is a causal factor for malnutrition (undernutrition). The available evidence on this causal relationship is based on observational studies and suffers from confounding and collider biases. This study aimed to construct a theoretical causal model to estimate the effect of SGA on malnutrition in children under five years of age.</p><p><strong>Methods: </strong>For the causal model, we designated term-SGA status as the exposure variable and malnutrition at six months to five years of age (diagnosed by WHO criteria) as the outcome variable. Causal estimands were formulated for three stakeholders. A 'rapid narrative review' methodology was adopted for literature synthesis. Studies (observational and randomized) listing the causal factors of malnutrition in children under five years of age from the Indian subcontinent were eligible. Four databases (PubMed, Scopus, Web of Science, and ProQuest) were searched and restricted to the last 10 years (search date: 15/12/2023). Information about the causal factors (covariates) of malnutrition and study characteristics was extracted from the article abstracts. Next, a causal model in the form of a directed acyclic graph (DAG) [DAGitty software] was constructed by connecting exposure, outcome, and covariate nodes using the sequential causal criteria of temporality, face validity, recourse to theory, and counterfactual thought experiments.</p><p><strong>Results: </strong>The search yielded 4818 records, of which 342 abstracts were included. Most of the studies were conducted in India (39%) and Bangladesh (27%). The literature synthesis identified 81 factors that were grouped into seventeen nodes, referring to five domains: socioeconomic, parental, child-related, environmental, and political. The DAG identified twelve different minimal sufficient adjustment sets (conditioning sets for regression analysis) to estimate the total effect of SGA on malnutrition.</p><p><strong>Conclusions: </strong>We offer an evidence-based causal diagram that will minimize bias due to improper selection of factors in studies focusing on malnutrition in term-SGA infants. The DAG and adjustment sets will facilitate the design and data analysis of future studies.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111611"},"PeriodicalIF":7.3,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142647853","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":"Yet another problem with systematic reviews: A living review update.","authors":"Lesley Uttley, Yuliang Weng, Louise Falzon","doi":"10.1016/j.jclinepi.2024.111608","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2024.111608","url":null,"abstract":"<p><p>In February 2023, the Journal of Clinical Epidemiology published 'The Problems with Systematic Reviews: A living Systematic Review.' In updating this living review for the first time to incorporate literature from May 2022 to May 2023, a new problem and several themes have emerged from 152 newly included articles relating to research culture This brings the total number of relevant articles up to 637 and the total number of problems with systematic reviews up to 68. This update documents a new problem: the lack of gender diversity of systematic review author teams. It also reveals emerging themes such as: fast science from systematic reviews on COVID-19; the failure of citation of methodological or reporting guidelines to predict high-quality methodological or reporting quality; and the influence of vested interests on systematic review conclusions. These findings coupled with a proliferation of research waste from \"me-too\" meta-research articles highlighting well-established problems in systematic reviews underscores the need for reforms in research culture to address the incentives for producing and publishing research papers. This update reports where the identified flaws in systematic reviews affect their conclusions drawing on 77 meta-epidemiological studies from the total 637 included articles. These meta-meta-analytic studies begin the important work of examining which problems threaten the reliability and validity of treatment effects or conclusions derived from systematic reviews. We recommend that meta-research endeavours evolve from merely documenting well-established issues to understanding lesser-known problems or consequences to systematic reviews.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111608"},"PeriodicalIF":7.3,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631900","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 definition of predictor and outcome variables in mortality prediction models: a scoping review and quality of reporting study\".","authors":"Eline G M Cox, Daniek A M Meijs, Laure Wynants, Jan-Willem E M Sels, Jacqueline Koeze, Frederik Keus, Bianca Bos-van Dongen, Iwan C C van der Horst, Bas C T van Bussel","doi":"10.1016/j.jclinepi.2024.111605","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2024.111605","url":null,"abstract":"<p><strong>Background: </strong>Mortality prediction models are promising tools for guiding clinical decision-making and resource allocation in intensive care units (ICUs). Clearly specified predictor and outcome variables are necessary to enable external validation and safe clinical application of prediction models. The objective of this study was to identify the predictor and outcome variables used in different mortality prediction models in the ICU and investigate their reporting.</p><p><strong>Methods: </strong>For this scoping review, MEDLINE, EMBASE, Web of Science, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched. Studies developed within a general ICU population reporting on prediction models with mortality as a primary or secondary outcome were eligible. The selection criteria were adopted from a review by Keuning et al. Predictor and outcome variables, variable characteristics (defined as units, definitions, moments of measurement and methods of measurement), and publication details (defined as first author, year of publication and title) were extracted from the included studies. Predictor and outcome variable categories were demographics, chronic disease, care logistics, acute diagnosis, clinical examination and physiological derangement, laboratory assessment, additional diagnostics, support and therapy, risk scores, and (mortality) outcomes.</p><p><strong>Results: </strong>A total of 56 mortality prediction models containing 204 unique predictor and outcome variables were included. The predictor variables most frequently included in the models were age (40 times), admission type (27 times), and mechanical ventilation (21 times). We observed that single variables were measured with different units, according to different definitions, at a different moment, and with a different method of measurement in different studies. The reporting of the unit was mostly complete (98% overall, 95% in the laboratory assessment category), whereas the definition of the variable (74% overall, 63% in the chronic disease category) and method of measurement (70% overall, 34% in the demographics category) were most often lacking.</p><p><strong>Conclusions: </strong>Accurate and transparent reporting of predictor and outcome variables is paramount to enhance reproducibility, model performance in different contexts, and validity. Since unclarity about the required input data may introduce bias and thereby affect model performance, this study advocates that prognostic ICU models can be improved by transparent and clear reporting of predictor and outcome variables and their characteristics.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111605"},"PeriodicalIF":7.3,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631589","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":"Editing companies have the responsibility of ensuring their declared use of generative artificial intelligence.","authors":"Jaime A Teixeira da Silva","doi":"10.1016/j.jclinepi.2024.111607","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2024.111607","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111607"},"PeriodicalIF":7.3,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631592","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":"Methodological Quality Assessment Tools for Diagnosis and Prognosis Research: Overview and Guidance.","authors":"Tabea Kaul, Bas E Kellerhuis, Johanna Aa Damen, Ewoud Schuit, Kevin Jenniskens, Maarten van Smeden, Johannes B Reitsma, Lotty Hooft, Karel Gm Moons, Bada Yang","doi":"10.1016/j.jclinepi.2024.111609","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2024.111609","url":null,"abstract":"<p><strong>Background and objective: </strong>Multiple tools exist for assessing the methodological quality of diagnosis and prognosis research. It can be challenging to decide on when to use which tool. We aimed to provide an overview of existing methodological quality assessment (QA) tools for diagnosis and prognosis studies, highlight the overlap and differences among these tools, and to provide guidance for choosing the appropriate tool.</p><p><strong>Study design and setting: </strong>We performed a methodological review of tools designed for assessing risk of bias, applicability, or other aspects related to methodological quality in studies investigating tests/factors/markers/models for classifying or predicting a current (diagnosis) and/or future (prognosis) health state. Tools focusing exclusively on causal research or on reporting quality were excluded. Guidance was subsequently developed to assist in choosing an appropriate QA tool.</p><p><strong>Results: </strong>We identified 14 QA tools, eight of which were developed for assessment of diagnosis studies, four for prognosis studies, and two addressing both. We propose a set of five questions to help guide the process of choosing a QA tool based on the purpose or question of the user: whether the focus is on (1) diagnosis, prognosis, or another domain; (2) a prediction model versus a test/factor/marker; (3) evaluating simply the performance of a test/factor/marker versus assessing its added value over other variables; (4) comparing two or more tests/factors/markers/models; and (5) whether the user aims to assess only risk of bias or also other quality aspects.</p><p><strong>Conclusion: </strong>Existing QA tools for appraising diagnosis and prognosis studies vary in purpose, scope, and contents. Our guidance may help researchers, systematic reviewers, health policy makers, and guideline developers in specifying their purpose and question to select the most appropriate QA tool for their assessment.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111609"},"PeriodicalIF":7.3,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631595","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":"Sociodemographic bias in clinical machine learning models: A scoping review of algorithmic bias instances and mechanisms.","authors":"Michael Colacci, Yu Qing Huang, Gemma Postill, Pavel Zhelnov, Orna Fennelly, Amol Verma, Sharon Straus, Andrea C Tricco","doi":"10.1016/j.jclinepi.2024.111606","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2024.111606","url":null,"abstract":"<p><strong>Background: </strong>Clinical machine learning (ML) technologies can sometimes be biased and their use could exacerbate health disparities. The extent to which bias is present, the groups who most frequently experience bias, and the mechanism through which bias is introduced in clinical ML applications is not well described. The objective of this study was to examine instances of bias in clinical ML models. We identified the sociodemographic subgroups (using the PROGRESS-Plus framework) that experienced bias and the reported mechanisms of bias introduction METHODS: We searched MEDLINE, EMBASE, PsycINFO and Web of Science for all studies that evaluated bias on sociodemographic factors within ML algorithms created for the purpose of facilitating clinical care. The scoping review was conducted according to the JBI guide and reported using the PRISMA extension for scoping reviews.</p><p><strong>Results: </strong>We identified 6448 articles, of which 760 reported on a clinical ML model and 91 (12.0%) completed a bias evaluation and met all inclusion criteria. Most studies evaluated a single sociodemographic factor (n=56, 61.5%). The most frequently evaluated sociodemographic factor was race (n=59, 64.8%), followed by sex/gender (n=41, 45.1%), and age (n=24, 26.4%), with one study (1.1%) evaluating intersectional factors. Of all studies, 74.7% (n=68) reported that bias was present, 18.7% (n=17) reported bias was not present, and 6.6% (n=6) did not state whether bias was present. When present, 87% of studies reported bias against groups with socioeconomic disadvantage.</p><p><strong>Conclusion: </strong>Most ML algorithms that were evaluated for bias demonstrated bias on sociodemographic factors. Furthermore, most bias evaluations concentrated on race, sex/gender, and age, while other sociodemographic factors and their intersection were infrequently assessed. Given potential health equity implications, bias assessments should be completed for all clinical ML models.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111606"},"PeriodicalIF":7.3,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631723","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}