Indian contribution toward biomedical research and development in COVID-19: A systematic review.
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ABSTRACT:
SUBMITTER: Kaur H
PROVIDER: S-EPMC8216129 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThere is an increasing need to evaluate the production and impact of medical research produced by institutions. Many indicators exist, yet we do not have enough information about their relevance. The objective of this systematic review was (1) to identify all the indicators that could be used to measure the output and outcome of medical research carried out in institutions and (2) enlist their methodology, use, positive and negative points.MethodologyWe have searched 3 databases (Pubmed, Scopus, Web of Science) using the following keywords: [Research outcome* OR research output* OR bibliometric* OR scientometric* OR scientific production] AND [indicator* OR index* OR evaluation OR metrics]. We included articles presenting, discussing or evaluating indicators measuring the scientific production of an institution. The search was conducted by two independent authors using a standardised data extraction form. For each indicator we extracted its definition, calculation, its rationale and its positive and negative points. In order to reduce bias, data extraction and analysis was performed by two independent authors.FindingsWe included 76 articles. A total of 57 indicators were identified. We have classified those indicators into 6 categories: 9 indicators of research activity, 24 indicators of scientific production and impact, 5 indicators of collaboration, 7 indicators of industrial production, 4 indicators of dissemination, 8 indicators of health service impact. The most widely discussed and described is the h-index with 31 articles discussing it.DiscussionThe majority of indicators found are bibliometric indicators of scientific production and impact. Several indicators have been developed to improve the h-index. This indicator has also inspired the creation of two indicators to measure industrial production and collaboration. Several articles propose indicators measuring research impact without detailing a methodology for calculating them. Many bibliometric indicators identified have been created but have not been used or further discussed.
| S-EPMC4383328 | biostudies-literature
Project description:Coronaviruses (CoV) cause respiratory and intestinal infections. We conducted this bibliometric analysis and systematical review to explore the CoV-related research trends from before COVID-19. We systematically searched the Ovid MEDLINE, Ovid Embase, and Web of Science (WOS) databases for published bibliometric analyses of CoV from database inception to January 24, 2021. The WOS Collection was searched from inception to January 31, 2020, to acquire the CoV-related publications before COVID-19. One-Way ANOVA and Bonferroni multiple-comparison tests were used to compare differences. Visualization mapping and keyword cluster graphs were made to illustrate the research topics and hotpots. We included 14,141 CoV-related publications for the bibliometric analysis and 16 (12 articles) CoV-related bibliometric analyses for the systematic review. Both the systematic review and bibliometric analysis showed (1) the number of publications showed two steep upward trajectories in 2003-2004 and in 2012-2014; (2) the research hotpots mainly focused on the mechanism, pathology, epidemiology, clinical diagnosis, and treatment of the coronavirus in MERS-CoV and SARS-Cov; (3) the USA, and China; the University of Hong Kong; and Yuen KY, came from the University of Hong Kong contributed most; (4) the Journal of Virology had the largest number of CoV related studies. More studies should focus on prevention, diagnosis, and treatment in the future.
| S-EPMC8635101 | biostudies-literature
Project description:Purpose: This study examined the gender composition of career development award applicants and grant review panels during the pandemic compared with that beforehand. Methods: Data were collected from 14 Health Research Alliance (HRA) organizations, which fund biomedical research and training. HRA members provided the gender of grant applicants and grant reviewers during the pandemic (April 1, 2020, to February 28, 2021) and prepandemic (April 1, 2019, to February 29, 2020). The signed-rank test compared medians and the chi square test compared the overall gender distribution. Results: The total number of applicants was similar during the pandemic (N = 3,724) and prepandemic (N = 3,882) periods, as was the percentage of women applicants (45.2% pandemic vs. 44.9% prepandemic, p = 0.78). The total number of men and women grant reviewers declined during the pandemic (N = 856) compared with that pre-pandemic (N = 1,689); this decrease was driven by a change for the largest funder. Also driven by changes for this one funder, the percentage of total grant reviewers who were women increased significantly during the pandemic (45.9%) compared with that during prepandemic (38.8%; p = 0.001), but the median percentage of women grant reviewers across organizations remained similar during the pandemic (43.6%) and prepandemic periods (38.2%; p = 0.53). Conclusions: In a sample of research organizations, the gender composition of grant applicants and grant review panels remained similar, except for the review panel composition for one large funder. Given evidence from other studies that have revealed gender differences in other career and life experiences of scientists during the pandemic, ongoing evaluation of women's representation in grant submission and review mechanisms is essential.
| S-EPMC10329151 | biostudies-literature
Project description:BackgroundSex and gender are believed to influence vaccine response. Yet, the relationship between sex and gender and COVID-19 vaccine efficacy is poorly understood and remains under-investigated.MethodsWe conducted a systematic review to determine whether and to what extent post-approval COVID-19 vaccine effectiveness (VE) studies report sex-disaggregated VE data. We searched four publication and pre-publication databases and additional grey literature sources for relevant published/preprint studies released between 1 January 2020 and 1 October 2021 (i.e., pre-Omicron era). We included observational studies providing VE estimates for one or more licensed/approved COVID-19 vaccines and including both males and females. Two reviewers independently assessed study eligibility, extracted data, and assessed risk-of-bias through a modified version of Cochrane's ROBINS-I tool. A qualitative data synthesis was performed.ResultsHere we show that, among 240 eligible publications, 68 (28.3%) do not report the sex distribution among participants. Only 21/240 (8.8%) studies provide sex-disaggregated VE estimates, and high between-study heterogeneity regarding design, target population, outcomes, and vaccine type/timing prevent the assessment of sex in determining COVID-19 VE across studies.ConclusionsOur findings indicate that few COVID-19 vaccine research publications account for sex. Improved adherence to recommended reporting guidelines will ensure that the evidence generated can be used to better understand the relationship between sex and gender and VE.
| S-EPMC10196298 | biostudies-literature
Project description:The amount of information for clinicians and clinical researchers is growing exponentially. Text summarization reduces information as an attempt to enable users to find and understand relevant source texts more quickly and effortlessly. In recent years, substantial research has been conducted to develop and evaluate various summarization techniques in the biomedical domain. The goal of this study was to systematically review recent published research on summarization of textual documents in the biomedical domain.MEDLINE (2000 to October 2013), IEEE Digital Library, and the ACM digital library were searched. Investigators independently screened and abstracted studies that examined text summarization techniques in the biomedical domain. Information is derived from selected articles on five dimensions: input, purpose, output, method and evaluation.Of 10,786 studies retrieved, 34 (0.3%) met the inclusion criteria. Natural language processing (17; 50%) and a hybrid technique comprising of statistical, Natural language processing and machine learning (15; 44%) were the most common summarization approaches. Most studies (28; 82%) conducted an intrinsic evaluation.This is the first systematic review of text summarization in the biomedical domain. The study identified research gaps and provides recommendations for guiding future research on biomedical text summarization.Recent research has focused on a hybrid technique comprising statistical, language processing and machine learning techniques. Further research is needed on the application and evaluation of text summarization in real research or patient care settings.
| S-EPMC4261035 | biostudies-literature
Project description:Digitization of a health record changes its accessibility. An electronic health record (EHR) can be accessed by multiple authorized users. Health information from EHRs contributes to learning healthcare systems' development. The objective of this systematic review is to answer a question: What are ethical issues concerning research using EHRs in the literature? We searched Medline Ovid, Embase and Scopus for publications concerning ethical issues of research use of EHRs. We employed the constant comparative method to retrieve common ethical themes. We descriptively summarized empirical studies. The study reveals the breadth, depth, and complexity of ethical problems associated with research use of EHRs. The central ethical question that emerges from the review is how to manage access to EHRs. Managing accessibility consists of interconnected and overlapping issues: streamlining research access to EHRs, minimizing risk, engaging and educating patients, as well as ensuring trustworthy governance of EHR data. Most of the ethical problems concerning EHR-based research arise from rapid cultural change. The framing of concepts of privacy, as well as individual and public dimensions of beneficence, are changing. We are currently living in the middle of this transition period. Human emotions and mental habits, as well as laws, are lagging behind technological developments. In the medical tradition, individual patient's health has always been in the center. Transformation of healthcare care, its digitalization, seems to have some impacts on our perspective of health care ethics, research ethics and public health ethics.
| S-EPMC8214390 | biostudies-literature
Project description:BackgroundInformed consent is one of the key elements in biomedical research. The introduction of electronic informed consent can be a way to overcome many challenges related to paper-based informed consent; however, its novel opportunities remain largely unfulfilled due to several barriers.ObjectiveWe aimed to provide an overview of the ethical, legal, regulatory, and user interface perspectives of multiple stakeholder groups in order to assist responsible implementation of electronic informed consent in biomedical research.MethodsWe conducted a systematic literature search using Web of Science (Core collection), PubMed, EMBASE, ACM Digital Library, and PsycARTICLES. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were used for reporting this work. We included empirical full-text studies focusing on the concept of electronic informed consent in biomedical research covering the ethical, legal, regulatory, and user interface domains. Studies written in English and published from January 2010 onward were selected. We explored perspectives of different stakeholder groups, in particular researchers, research participants, health authorities, and ethics committees. We critically appraised literature included in the systematic review using the Newcastle-Ottawa scale for cohort and cross-sectional studies, Critical Appraisal Skills Programme for qualitative studies, Mixed Methods Appraisal Tool for mixed methods studies, and Jadad tool for randomized controlled trials.ResultsA total of 40 studies met our inclusion criteria. Overall, the studies were heterogeneous in the type of study design, population, intervention, research context, and the tools used. Most of the studies' populations were research participants (ie, patients and healthy volunteers). The majority of studies addressed barriers to achieving adequate understanding when using electronic informed consent. Concerns shared by multiple stakeholder groups were related to the security and legal validity of an electronic informed consent platform and usability for specific groups of research participants.ConclusionsElectronic informed consent has the potential to improve the informed consent process in biomedical research compared to the current paper-based consent. The ethical, legal, regulatory, and user interface perspectives outlined in this review might serve to enhance the future implementation of electronic informed consent.Trial registrationPROSPERO International Prospective Register of Systematic Reviews CRD42020158979; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=158979.
| S-EPMC7582148 | biostudies-literature
Project description:BackgroundThe use of great apes (GA) in invasive biomedical research is one of the most debated topics in animal ethics. GA are, thus far, the only animal group that has frequently been banned from invasive research; yet some believe that these bans could inaugurate a broader trend towards greater restrictions on the use of primates and other animals in research. Despite ongoing academic and policy debate on this issue, there is no comprehensive overview of the reasons advanced for or against restricting invasive research with GA. To address this gap, we conducted a systematic review of the reasons reported in the academic literature on this topic.MethodsSeven databases were searched for articles published in English. Two authors screened the titles, abstracts, and full texts of all articles. Two journals specialized in animal ethics, and the reference lists of included articles were subsequently also reviewed.ResultsWe included 60 articles, most of which were published between 2006 and 2016. Twenty-five articles argued for a total ban of GA research, 21 articles defended partial restrictions, and 14 articles argued against restrictions. Overall, we identified 110 reason types, 74 for, and 36 against, restricting GA research. Reasons were grouped into nine domains: moral standing, science, welfare, public and expert attitudes, retirement and conservation, respect and rights, financial costs, law and legal status, and longer-term consequences.ConclusionOur review generated five main findings. First, there is a trend in the academic debate in favor of restricting GA research that parallels worldwide policy changes in the same direction. Second, in several domains (e.g., moral standing, and respect and rights), the reasons were rather one-sided in favor of restrictions. Third, some prominent domains (e.g., science and welfare) featured considerable engagement between opposing positions. Fourth, there is low diversity and independence among authors, including frequent potential conflicts of interests in articles defending a strong position (i.e., favoring a total ban or arguing against restrictions). Fifth, scholarly discussion was not the norm, as reflected in a high proportion of non-peer-reviewed articles and authors affiliated to non-academic institutions.
| S-EPMC7888082 | biostudies-literature
Project description:ObjectivesThe aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation period of COVID-19.DesignRapid systematic review and meta-analysis of observational research.SettingInternational studies on incubation period of COVID-19.ParticipantsSearches were carried out in PubMed, Google Scholar, Embase, Cochrane Library as well as the preprint servers MedRxiv and BioRxiv. Studies were selected for meta-analysis if they reported either the parameters and CIs of the distributions fit to the data, or sufficient information to facilitate calculation of those values. After initial eligibility screening, 24 studies were selected for initial review, nine of these were shortlisted for meta-analysis. Final estimates are from meta-analysis of eight studies.Primary outcome measuresParameters of a lognormal distribution of incubation periods.ResultsThe incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters (95% CIs) of 1.63 (95% CI 1.51 to 1.75) and 0.50 (95% CI 0.46 to 0.55), respectively. The corresponding mean (95% CIs) was 5.8 (95% CI 5.0 to 6.7) days. It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates (95% CIs) resulted in a median incubation period of 5.1 (95% CI 4.5 to 5.8) days, whereas the 95th percentile was 11.7 (95% CI 9.7 to 14.2) days.ConclusionsThe choice of which parameter values are adopted will depend on how the information is used, the associated risks and the perceived consequences of decisions to be taken. These recommendations will need to be revisited once further relevant information becomes available. Accordingly, we present an R Shiny app that facilitates updating these estimates as new data become available.
| S-EPMC7430485 | biostudies-literature
Project description:SARS-CoV-2 has infected over ∼165 million people worldwide causing Acute Respiratory Distress Syndrome (ARDS) and has killed ∼3.4 million people. Artificial Intelligence (AI) has shown to benefit in the biomedical image such as X-ray/Computed Tomography in diagnosis of ARDS, but there are limited AI-based systematic reviews (aiSR). The purpose of this study is to understand the Risk-of-Bias (RoB) in a non-randomized AI trial for handling ARDS using novel AtheroPoint-AI-Bias (AP(ai)Bias). Our hypothesis for acceptance of a study to be in low RoB must have a mean score of 80% in a study. Using the PRISMA model, 42 best AI studies were analyzed to understand the RoB. Using the AP(ai)Bias paradigm, the top 19 studies were then chosen using the raw-cutoff of 1.9. This was obtained using the intersection of the cumulative plot of "mean score vs. study" and score distribution. Finally, these studies were benchmarked against ROBINS-I and PROBAST paradigm. Our observation showed that AP(ai)Bias, ROBINS-I, and PROBAST had only 32%, 16%, and 26% studies, respectively in low-moderate RoB (cutoff>2.5), however none of them met the RoB hypothesis. Further, the aiSR analysis recommends six primary and six secondary recommendations for the non-randomized AI for ARDS. The primary recommendations for improvement in AI-based ARDS design inclusive of (i) comorbidity, (ii) inter-and intra-observer variability studies, (iii) large data size, (iv) clinical validation, (v) granularity of COVID-19 risk, and (vi) cross-modality scientific validation. The AI is an important component for diagnosis of ARDS and the recommendations must be followed to lower the RoB.
| S-EPMC8843049 | biostudies-literature