BMJ Surgery Interventions Health Technologies最新文献

{"title":"Development of a core minimum data set to advance real-world evidence generation for uterine fibroids treatment technologies.","authors":"Courtney E Baird, Evan Myers, Vanessa Jacoby, Laura Elisabeth Gressler, Sateria Venable, Allison O'Neill, Veronica Price, Angie Lee, Jason Roberts, Sharon Andrews, Art Sedrakyan, Danica Marinac-Dabic","doi":"10.1136/bmjsit-2021-000094","DOIUrl":"10.1136/bmjsit-2021-000094","url":null,"abstract":"<p><strong>Objectives: </strong>The accumulation of data through a prospective, multicenter coordinated registry network (CRN) is a robust and cost-effective way to gather real-world evidence on the performance of uterine fibroids treatment technologies for device-based and intervention-based studies. To develop the CRN, a group of uterine fibroids experts, consisting of representatives from professional societies, the US Food and Drug Administration, academia, industry, and the patient community, was convened to discuss the role and feasibility of the CRN and to identify the core data elements needed to assess uterine fibroid treatment technologies.</p><p><strong>Design: </strong>A Delphi method approach was employed to achieve consensus on a core minimum data set for the CRN. A series of surveys were sent to the panel and answered by each expert anonymously and individually. Results from the surveys were collected, collated, and analyzed by a study design team from Weill Cornell Medicine. Questions for the next round were based on the analysis process and discussed with group members via a conference call. This process was repeated twice over a 3-month time period until consensus was achieved.</p><p><strong>Results: </strong>Twenty-nine experts participated in the Delphi surveys, which began with an initial list of 200 data elements. The working group reached final consensus on 97 data elements capturing patient medical history, imaging data, procedure-related data, post-procedure data, and long-term follow-up data.</p><p><strong>Conclusions: </strong>The CRN successfully convened an expert panel on uterine fibroids treatment technologies and used the Delphi method to produce a consensus-based core set of data elements. These identified data elements include important outcomes related to efficacy and safety and thus, influence patient, provider, and regulatory decision-making about treatments for uterine fibroids. Finally, the core data elements provide the foundation of the infrastructure needed for the CRN that will allow for the comparative study of uterine fibroid treatment devices and technologies.</p>","PeriodicalId":33349,"journal":{"name":"BMJ Surgery Interventions Health Technologies","volume":"4 Suppl 1","pages":"e000094"},"PeriodicalIF":2.1,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/de/a6/bmjsit-2021-000094.PMC9660574.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9723703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the development status of intraoperative fluorescence imaging for anatomy visualisation, using the IDEAL framework.","authors":"Takeaki Ishizawa,&nbsp;Peter McCulloch,&nbsp;Laurents Stassen,&nbsp;Jacqueline van den Bos,&nbsp;Jean-Marc Regimbeau,&nbsp;Jeanne Dembinski,&nbsp;Sylke Schneider-Koriath,&nbsp;Luigi Boni,&nbsp;Takeshi Aoki,&nbsp;Hiroto Nishino,&nbsp;Kiyoshi Hasegawa,&nbsp;Yasuo Sekine,&nbsp;Toyofumi Chen-Yoshikawa,&nbsp;Trevor Yeung,&nbsp;Eren Berber,&nbsp;Bora Kahramangil,&nbsp;Michael Bouvet,&nbsp;Michele Diana,&nbsp;Norihiro Kokudo,&nbsp;Fernando Dip,&nbsp;Kevin White,&nbsp;Raul J Rosenthal","doi":"10.1136/bmjsit-2022-000156","DOIUrl":"https://doi.org/10.1136/bmjsit-2022-000156","url":null,"abstract":"<p><strong>Objectives: </strong>Intraoperative fluorescence imaging is currently used in a variety of surgical fields for four main purposes: visualising anatomy, assessing tissue perfusion, identifying/localising cancer and mapping lymphatic systems. To establish evidence-based guidance for research and practice, understanding the state of research on fluorescence imaging in different surgical fields is needed. We evaluated the evidence on fluorescence imaging used to visualise anatomical structures using the IDEAL framework, a framework designed to describe the stages of innovation in surgery and other interventional procedures.</p><p><strong>Design: </strong>IDEAL staging based on a thorough literature review.</p><p><strong>Setting: </strong>All publications on intraoperative fluorescence imaging for visualising anatomical structures reported in PubMed through 2020 were identified for five surgical procedures: cholangiography, hepatic segmentation, lung segmentation, ureterography and parathyroid identification.</p><p><strong>Main outcome measures: </strong>The IDEAL stage of research evidence was determined for each of the five procedures using a previously described approach.</p><p><strong>Results: </strong>225 articles (8427 cases) were selected for analysis. Current status of research evidence on fluorescence imaging was rated IDEAL stage 2a for ureterography and lung segmentation, IDEAL 2b for hepatic segmentation and IDEAL stage 3 for cholangiography and parathyroid identification. Enhanced tissue identification rates using fluorescence imaging relative to conventional white-light imaging have been documented for all five procedures by comparative studies including randomised controlled trials for cholangiography and parathyroid identification. Advantages of anatomy visualisation with fluorescence imaging for improving short-term and long-term postoperative outcomes also were demonstrated, especially for hepatobiliary surgery and (para)thyroidectomy. No adverse reactions associated with fluorescent agents were reported.</p><p><strong>Conclusions: </strong>Intraoperative fluorescence imaging can be used safely to enhance the identification of anatomical structures, which may lead to improved postoperative outcomes. Overviewing current research knowledge using the IDEAL framework aids in designing further studies to develop fluorescence imaging techniques into an essential intraoperative navigation tool in each surgical field.</p>","PeriodicalId":33349,"journal":{"name":"BMJ Surgery Interventions Health Technologies","volume":" ","pages":"e000156"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/58/15/bmjsit-2022-000156.PMC9639126.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40454216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining the empirical evidence for IDEAL 2b studies: the effects of preceding prospective collaborative cohort studies on the quality and impact of subsequent randomized controlled trials of surgical innovations - protocol for a systematic review and case-control analysis.","authors":"Mudathir Ibrahim,&nbsp;Arsenio Paez,&nbsp;Jiajie Yu,&nbsp;Baptiste Vasey,&nbsp;Joel Horovitz,&nbsp;Peter McCulloch","doi":"10.1136/bmjsit-2021-000120","DOIUrl":"https://doi.org/10.1136/bmjsit-2021-000120","url":null,"abstract":"<p><p>Randomized controlled trials (RCTs) in surgery face methodological challenges, which often result in low quality or failed trials. The Idea, Development, Exploration, Assessment and Long-term (IDEAL) framework proposes preliminary prospective collaborative cohort studies with specific properties (IDEAL 2b studies) to increase the quality and feasibility of surgical RCTs. Little empirical evidence exists for this proposition, and specifically designed 2b studies are currently uncommon. Prospective collaborative cohort studies are, however, relatively common, and might provide similar benefits. We will, therefore, assess the association between prior 'IDEAL 2b-like' cohort studies and the quality and impact of surgical RCTs. We propose a systematic review using two parallel case-control analyses, with surgical RCTs as subjects and study quality and journal impact factor (IF) as the outcomes of interest. We will search for surgical RCTs published between 2015 and 2019 and and prior prospective collaborative cohort studies authored by any of the RCT investigators. RCTs will be categorized into cases or controls by (1) journal (IF ≥or <5) and (2) study quality (PEDro score ≥or < 7). The case/control OR of exposure to a prior '2b like' study will be calculated independently for quality and impact. Cases will be matched 1: 1 with controls by year of publication, and confounding by peer-reviewed funding, author academic affiliation and trial protocol registration will be examined using multiple logistic regression analysis. This study will examine whether preparatory IDEAL 2b-like studies are associated with higher quality and impact of subsequent RCTs.</p>","PeriodicalId":33349,"journal":{"name":"BMJ Surgery Interventions Health Technologies","volume":" ","pages":"e000120"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/3a/9c/bmjsit-2021-000120.PMC9639119.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40454217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Evaluation of intervertebral body implant performance using active surveillance of electronic health records.","authors":"","doi":"10.1136/bmjsit-2021-000125corr2","DOIUrl":"https://doi.org/10.1136/bmjsit-2021-000125corr2","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1136/bmjsit-2021-000125.].</p>","PeriodicalId":33349,"journal":{"name":"BMJ Surgery Interventions Health Technologies","volume":" ","pages":"e000125corr2"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/71/9e/bmjsit-2021-000125corr2.PMC9582312.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40653007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COP27 climate change conference: urgent action needed for Africa and the world.","authors":"Chris Zielinski","doi":"10.1136/bmjsit-2022-000174","DOIUrl":"https://doi.org/10.1136/bmjsit-2022-000174","url":null,"abstract":"","PeriodicalId":33349,"journal":{"name":"BMJ Surgery Interventions Health Technologies","volume":" ","pages":"e000174"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9582307/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40653005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Survival, reintervention and surveillance reports: long-term, centre-level evaluation and feedback of vascular interventions.","authors":"Xavier Philip Fowler,&nbsp;Barbara Gladders,&nbsp;Kayla Moore,&nbsp;Jialin Mao,&nbsp;Art Sedrakyan,&nbsp;Philip Goodney","doi":"10.1136/bmjsit-2022-000140","DOIUrl":"https://doi.org/10.1136/bmjsit-2022-000140","url":null,"abstract":"<p><p>The combination of registry and administrative claims data have facilitated research and quality improvement efforts. Using Vascular Quality Initiative (VQI) registry data and Medicare claims we have generated centre-specific survival, reintervention and surveillance reports which benchmark participating centres' performance to the VQI as a whole and to published guidelines. In 2021, we distributed these reports to 303 participating centres. These reports offer an opportunity for centres to evaluate their performance and identify focus areas for quality improvement work.</p>","PeriodicalId":33349,"journal":{"name":"BMJ Surgery Interventions Health Technologies","volume":" ","pages":"e000140"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/1a/46/bmjsit-2022-000140.PMC9557801.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33515268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Evaluation of intervertebral body implant performance using active surveillance of electronic health records.","authors":"","doi":"10.1136/bmjsit-2021-000125corr1","DOIUrl":"https://doi.org/10.1136/bmjsit-2021-000125corr1","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1136/bmjsit-2021-000125.].</p>","PeriodicalId":33349,"journal":{"name":"BMJ Surgery Interventions Health Technologies","volume":" ","pages":"e000125corr1"},"PeriodicalIF":0.0,"publicationDate":"2022-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e7/51/bmjsit-2021-000125corr1.PMC9362785.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40634650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Creation of objective performance criteria among medical devices.","authors":"Laura Elisabeth Gressler,&nbsp;Danica Marinac-Dabic,&nbsp;Susan dosReis,&nbsp;Philip Goodney,&nbsp;C Daniel Mullins,&nbsp;Fadia Shaya","doi":"10.1136/bmjsit-2021-000106","DOIUrl":"https://doi.org/10.1136/bmjsit-2021-000106","url":null,"abstract":"<p><strong>Objectives: </strong>Objective performance criteria (OPC) may serve as a tool to expedite the approval process and continue active surveillance of class III medical devices. Thus far, published guidance on the creation of OPC has been clinical area-specific. This study aimed to capture reflections from key stakeholders on the creation of OPC that may serve as a precursor for a formalized conceptual framework within the USA.</p><p><strong>Design: </strong>Reflections from key stakeholders and guidance from an advisory committee were captured to gain an understanding of the elements that are crucial to the generation of OPC.</p><p><strong>Setting: </strong>A non-probability sampling method using the purposive sampling strategy was employed to identify relevant stakeholders for engagement in semi-structured, open-ended, concept elicitation discussions.</p><p><strong>Participants: </strong>Stakeholders involved in the generation of OPC.</p><p><strong>Main outcome measures: </strong>Elements and themes regarding the priorities of, experiences with, roles within and perceived challenges associated with OPC creation captured through a phenomenological approach.</p><p><strong>Results: </strong>A total of 27 participants were engaged to represent the following contributors: representatives of registries, health systems, health technology assessment bodies, clinicians, device application reviewers, payers, patients, patient representatives, patient caregivers, device manufacturers, data coordinators, data analysts and data informaticians. Consensus was achieved on the five core elements: (1) identification of medical devices, (2) engagement of key stakeholders, (3) selection of data source, (4) performance of appropriate statistical analyses and (5) reporting of findings. The engagement of key stakeholders (38%) was cited most frequently as the most important core element. Access to meaningful and high-quality data sources (47%) was the most frequently mentioned challenge.</p><p><strong>Conclusions: </strong>The reflections from the participants identified five elements to be considered when generating an OPC within class III medical devices and may provide the needed foundation for the development of official guidance on OPC generation.</p>","PeriodicalId":33349,"journal":{"name":"BMJ Surgery Interventions Health Technologies","volume":" ","pages":"e000106"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/db/7c/bmjsit-2021-000106.PMC9345051.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40644443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing the reporting chasm of artificial intelligence research: the DECIDE-AI reporting guidelines.","authors":"John Gerrard Hanrahan,&nbsp;Danyal Zaman Khan,&nbsp;Hani J Marcus","doi":"10.1136/bmjsit-2022-000154","DOIUrl":"https://doi.org/10.1136/bmjsit-2022-000154","url":null,"abstract":"© Author(s) (or their employer(s)) 2022. Reuse permitted under CC BYNC. No commercial reuse. See rights and permissions. Published by BMJ. EDITORIAL The meteoric rise of artificial intelligence (AI) to the forefront of healthcare innovation has unearthed an array of avenues for surgical researchers to pursue. Applications found throughout the surgical patient pathway mean AI offers newfound support systems for clinical decisionmaking. Indeed, a growing number of technologies are entering clinical practice, with a recent review evaluating randomised controlled trials of diagnostic prediction tools suggests that potential benefits of AI that contemporary healthcare stands to realise. However, the pathway to translation to the bedside for these technologies is variable. Captured aptly in a recent editorial, there are clear examples of AI technologies already approved for clinical use in the USA, both with and without evaluation through randomised controlled trials. This speaks to a wider problem of evaluation in AI innovation, where insufficient reporting in randomised controlled trials prompted the development of several reporting guidelines, examples including the Consolidated Standards of Reporting TrialsAI and Standard Protocol Items: Recommendations for Interventional TrialsAI guidelines advising the minimum reporting standards for clinical trials and protocols, respectively. Similarly, guidance for the initial stages of AI development has been developed, namely, the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPODAI) guidelines for machine learning (ML) prediction models. Yet, when one looks at the process of AI translation, from in silico to clinical trial, an evaluation chasm becomes obvious, with guidance lacking on studies reflecting stages 2a and 2b of the IDEAL (Idea, Development, Exploration, Assessment, Longterm study) collaborative. These stages reflect the refinement and preparation for larger clinical studies, which are influenced by factors from the operator including learning curves or training; the health system the technologies enter into or organisational factors such as integration into clinical workflows. Study design features such as patient selection for both training and testing an intervention, and even the AI model itself, are crucial factors to consider prior to largescale testing. Vasey and colleagues have identified a gap in the reporting guidelines for evaluating AIdriven decision support systems, producing reporting guidelines to support the evaluation of their early stages. This was achieved through an international, tworound modified Delphi consensus process producing a 17 AIspecific item and 10 generic item reporting guidelines (DECIDEAI), informing the reporting of earlystage clinical studies of AIbased decision support systems in healthcare. The systems perspective taken by Vasey et al frame AI decisionsupport systems as complex interventions. This perspect","PeriodicalId":33349,"journal":{"name":"BMJ Surgery Interventions Health Technologies","volume":" ","pages":"e000154"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/51/52/bmjsit-2022-000154.PMC9345081.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40644442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Endovascular versus open repair in patients with abdominal aortic aneurysm: a claims-based data analysis in Japan.","authors":"Yuki Kimura,&nbsp;Hiroshi Ohtsu,&nbsp;Naohiro Yonemoto,&nbsp;Nobuyoshi Azuma,&nbsp;Kazuhiro Sase","doi":"10.1136/bmjsit-2022-000131","DOIUrl":"https://doi.org/10.1136/bmjsit-2022-000131","url":null,"abstract":"<p><strong>Objectives: </strong>Endovascular aortic repair (EVAR) evolved through competition with open aortic repair (OAR) as a safe and effective treatment option for appropriately selected patients with abdominal aortic aneurysm (AAA). Although endoleaks are the most common reason for post-EVAR reintervention, compliance with lifelong regular follow-up imaging remains a challenge.</p><p><strong>Design: </strong>Retrospective data analysis.</p><p><strong>Setting: </strong>The Japan Medical Data Center (JMDC), a claims database with anonymous data linkage across hospitals, consists of corporate employees and their families of ≤75 years of age.</p><p><strong>Participants: </strong>The analysis included participants in the JMDC who underwent EVAR or OAR for intact (iAAA) or ruptured (rAAA) AAA. Patients with less than 6 months of records before the aortic repair were excluded.</p><p><strong>Main outcome measures: </strong>Overall survival and reintervention rates.</p><p><strong>Results: </strong>We identified 986 cases (837 iAAA and 149 rAAA) from JMDC with first aortic repairs between January 2015 and December 2020. The number of patients, median age (years (IQR)), follow-up (months) and post-procedure CT scan (times per year) were as follows: iAAA (OAR: n=593, 62.0 (57.0-67.0), 26.0, 1.6, EVAR: n=244, 65.0 (31.0-69.0), 17.0, 2.2), rAAA (OAR: n=110, 59.0 (53.0-59.0), 16.0, 2.1, EVAR: n=39, 62.0 (31.0-67.0), 18.0, 2.4). Reintervention rate was significantly higher among EVAR than OAR in rAAA (15.4% vs 8.2%, p=0.04). In iAAA, there were no group difference after 5 years (7.8% vs 11.0%, p=0.28), even though EVAR had initial advantage. There were no differences in mortality rate between EVAR and OAR for either rAAA or iAAA.</p><p><strong>Conclusions: </strong>Claims-based analysis in Japan showed no statistically significant difference in 5-year survival rates of the OAR and EVAR groups. However, the reintervention rate of EVAR in rAAA was significantly higher, suggesting the need for regular post-EVAR follow-up with imaging. Therefore, international collaborations for long-term outcome studies with real-world data are warranted.</p>","PeriodicalId":33349,"journal":{"name":"BMJ Surgery Interventions Health Technologies","volume":" ","pages":"e000131"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/31/5d/bmjsit-2022-000131.PMC9345055.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40644444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}