scholarly journals 3D-Printed Models Applied in Medical Research Studies

10.5772/63942 ◽  
2016 ◽  
Author(s):  
Jorge Roberto Lopes dos Santos ◽  
Heron Werner ◽  
Bruno Alvares de Azevedo ◽  
Luiz Lanziotti ◽  
Elyzabeth Avvad Portari ◽  
...  
Keyword(s):  
Medical Research ◽  
3D Printed ◽  
Research Studies

scholarly journals Regulating impact on bystanders in clinical trials: An unsettled frontier

2019 ◽  
Vol 16 (5) ◽  
pp. 450-454
Author(s):  
Nir Eyal ◽  
Jonathan Kimmelman ◽  
Lisa G Holtzman ◽  
Marc Lipsitch
Keyword(s):  
At Risk ◽  
Clinical Trials ◽  
Medical Research ◽  
Research Ethics ◽  
Academic Scholarship ◽  
Ethics Guidelines ◽  
Study Participants ◽  
Research Ethics Guidelines ◽  
Research Studies

This article informally reviews key research ethics guidelines and regulations, academic scholarship, and research studies and finds wide variety in how they consider risk to bystanders in medical research (namely, non-participants whom studies nevertheless place at risk). Some of these key sources give no or very little consideration to bystanders, while others offer them the utmost protection (greater than they offer study participants). This unsettled frontier would benefit from a deeper investigation of the ethics of protecting research bystanders.

scholarly journals Characteristics of scientific articles on COVID-19 published during the initial three months of the pandemic: a meta-epidemiological study

2020 ◽  
Author(s):  
Nicola Di Girolamo ◽  
Reint Meursinge Reynders
Keyword(s):  
Decision Making ◽  
Medical Research ◽  
Observational Studies ◽  
Data Extraction ◽  
Swine Influenza ◽  
Original Research ◽  
Policy Makers ◽  
In Silico Studies ◽  
Research Studies

ABSTRACTImportanceThe COVID-19 pandemic has been characterized by an unprecedented amount of published scientific articles.ObjectiveTo assess the characteristics of articles published during the first 3 months of the COVID-19 pandemic and to compare it with articles published during 2009 H1N1 swine influenza pandemic.Data sourcesArticles on COVID-19 and on H1N1 swine influenza indexed in PubMed (Medline) during the first 3 months of these pandemics.Study selectionAny article published in the respective study periods that included any terminology related to COVID-19 or H1N1 in the title, abstract or full-text was eligible for inclusion. Articles that did not present an English abstract, as well as correspondence to previous research and erratum were excluded.Data Extraction and SynthesisTwo operators conducted the selection of articles and data extraction procedures independently. The article is reported following STROBE guidelines for observational studies.Main Outcomes and MeasuresPrevalence of primary and secondary articles. Prevalence of reporting of limitations in the abstracts.ResultsOf the 2482 articles retrieved, 1165 were included. Approximately half of them were secondary articles (575, 49.4%). Common primary articles were: human medical research (340, 59.1%), in silico studies (182, 31.7%) and in vitro studies (26, 4.5%). Of the human medical research, the vast majority were observational studies and cases series, followed by single case reports and one randomized controlled trial. Secondary articles were mainly reviews, viewpoints and editorials (373, 63.2%). The second largest category was guidelines or guidance articles, including 193 articles (32.7%), of which 169 were indications for specific departments, patients or procedures. Limitations were reported in 42 out of 1165 abstracts (3.6%), with 10 abstracts reporting actual methodological limitations.In a similar timeframe in 2009 there were 223 articles published on the H1N1 pandemic. As compared to that pandemic, during COVID-19 there were higher chances to publish reviews and guidance articles and lower chances to publish in vitro and animal research studies.Conclusions and RelevanceAs compared to the most recent pandemic, there is an overwhelming amount of information published on COVID-19. However, the majority of the articles published do not add significant information, possibly diluting the original information published. Also, only a negligible number of published articles reports limitations in the abstracts, hindering a rapid interpretation of their shortcomings.Protocol RegistrationOur protocol was registered in Open Science Framework: https://osf.io/eanzrKEY POINTSQuestionPatients, health care professionals, policy makers, and the general public want to know what has been published on COVID-19 and what quality of research was available for decision making.FindingsHalf of the publications with an abstract were original research studies, i.e., for every original research article (primary article) on COVID-19 there was at least one other article that discussed or summarized what was already known (secondary article). Only 3.6% of the abstracts reported a clear statement on the limitations of the article.MeaningClinicians and policy makers have to filter out a large body of secondary articles, which may slow down decision making during the COVID-19 pandemic.

scholarly journals Generating high-quality data abstractions from scanned clinical records: text-mining-assisted extraction of endometrial carcinoma pathology features as proof of principle

BMJ Open ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. e037740
Author(s):  
Anthony Nguyen ◽  
John O'Dwyer ◽  
Thanh Vu ◽  
Penelope M Webb ◽  
Sharon E Johnatty ◽  
...  
Keyword(s):  
Text Mining ◽  
Medical Research ◽  
Endometrial Carcinoma ◽  
Expert Knowledge ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Pathology Reports ◽  
Clinical Records ◽  
Research Studies

ObjectiveMedical research studies often rely on the manual collection of data from scanned typewritten clinical records, which can be laborious, time consuming and error prone because of the need to review individual clinical records. We aimed to use text mining to assist with the extraction of clinical features from complex text-based scanned pathology records for medical research studies.DesignText mining performance was measured by extracting and annotating three distinct pathological features from scanned photocopies of endometrial carcinoma clinical pathology reports, and comparing results to manually abstracted terms. Inclusion and exclusion keyword trigger terms to capture leiomyomas, endometriosis and adenomyosis were provided based on expert knowledge. Terms were expanded with character variations based on common optical character recognition (OCR) error patterns as well as negation phrases found in sample reports. The approach was evaluated on an unseen test set of 1293 scanned pathology reports originating from laboratories across Australia.SettingScanned typewritten pathology reports for women aged 18–79 years with newly diagnosed endometrial cancer (2005–2007) in Australia.ResultsHigh concordance with final abstracted codes was observed for identifying the presence of three pathology features (94%–98% F-measure). The approach was more consistent and reliable than manual abstractions, identifying 3%–14% additional feature instances.ConclusionKeyword trigger-based automation with OCR error correction and negation handling proved not only to be rapid and convenient, but also providing consistent and reliable data abstractions from scanned clinical records. In conjunction with manual review, it can assist in the generation of high-quality data abstractions for medical research studies.

scholarly journals Systematic review finds that appraisal tools for medical research studies address conflicts of interest superficially

2020 ◽  
Vol 120 ◽  
pp. 104-115 ◽  
Author(s):  
Andreas Lundh ◽  
Kristine Rasmussen ◽  
Lasse Østengaard ◽  
Isabelle Boutron ◽  
Lesley A. Stewart ◽  
...  
Keyword(s):  
Systematic Review ◽  
Medical Research ◽  
Conflicts Of Interest ◽  
Research Studies

Public Attitudes Regarding Willingness to Participate in Medical Research Studies

2000 ◽  
Vol 12 (2) ◽  
pp. 23-43 ◽  
Author(s):  
Jeanette M. Trauth ◽  
Donald Musa ◽  
Laura Siminoff ◽  
Ilene Katz Jewell ◽  
Edmund Ricci
Keyword(s):  
Medical Research ◽  
Public Attitudes ◽  
Willingness To Participate ◽  
Research Studies
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