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publicOct 2020View details →

The clinical impact of high-profile animal-based research reported in the UK national press: a detailed discussion of articles from 1995, and full search results from the Nexis database

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publicMay 2025View details →

Recruitment and retention strategies for improving representation in clinical research: A meta-synthesis

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publicMar 2021View details →

Clinician-researcher’s perspectives on clinical research during the COVID-19 pandemic

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publicMar 2020View details →

Are researchers moving away from animal models as a result of poor clinical translation in the field of stroke? an analysis of opinion papers

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dryad28/100

Intensive care unit patients' opinion on enrollment in clinical research: a multicenter survey - Study data bank

Background: In most emergency situations or severe illness, patients are unable to consent for clinical trial enrollment. In such circumstances, the decision about whether to participate in a scientific study or not is made by a legally designated representative. Objective: To address the willingness of patients admitted to the intensive care unit (ICU) to be enrolled in a scientific study as volunteers, and to assess the agreement between patients' and their legal representatives' opinion concerning enrollment in a scientific study. Methods: This survey was conducted in two hospitals in São Paulo, Brazil. Patients (≥18 years) with preserved cognitive functions accompanied by a surrogate admitted to the ICU were eligible for this study. A survey containing 28 questions for patients and 8 questions for surrogates was applied within the first 48h from ICU admission. The survey for patients comprised three sections: demographic characteristics, opinion about participation in clinical research and knowledge about the importance of research. The survey for legal representatives contained two sections: demographic characteristics and assessment of legal representatives' opinion in authorizing patients to be enrolled in research. Results: Between January 2017 and May 2018, 208 pairs of ICU patients and their respective legal representatives answered the survey. Out of 208 ICU patients answering the survey, 73.6% (153/208) were willing to be enrolled in the study as volunteers. Of those patients, 65.1% (97/149) would continue participating in a research even if their legal representative did not support their enrollment. Agreement between patients' and surrogates' opinion concerning participation was poor [Kappa=0.11 (IC95% -0.02 to 0.25)]. If a consent for study participation had been obtained, 69.1% (103/149) of patients would continue participating in the study until its conclusion, and 23.5% (35/149) would allow researchers to use data collected to date, but would withdraw from the study on that occasion. Conclusion: The majority of patients admitted to the ICU were willing to be enrolled in a scientific study as volunteers, also after a deferred informed consent procedure has been used. Nevertheless, contradictory opinions between patients and their and their legal representatives' concerning enrollment in a scientific study were often observed.

opencc-zeroJul 2020View details →

dryad28/100

Data from: Automatic recognition of self-acknowledged limitations in clinical research literature

Objective: To automatically recognize self-acknowledged limitations in clinical research publications to support efforts in improving research transparency. Materials and Methods: To develop our recognition methods, we used a set of 8,431 sentences from 1,197 PubMed Central articles. A subset of these sentences was manually annotated for training/testing and inter-annotator agreement was calculated. We cast the recognition problem as a binary classification task, in which we determine whether a given sentence from a publication discusses self-acknowledged limitations or not. We experimented with three methods: a rule-based approach based on document structure, supervised machine learning, and a semi-supervised method that uses self-training to expand the training set in order to improve classification performance. The machine learning algorithms used were logistic regression (LR) and support vector machines (SVM). Results: Annotators had good agreement in labeling limitation sentences (Krippendorff's α=0.781). Of the three methods used, the rule-based method yielded the best performance with 91.5% accuracy (95% CI [90.1-92.9]), while self-training with SVM led to a small improvement over fully supervised learning (89.9%, 95% CI [88.4-91.4] vs. 89.6%, 95% CI [88.1-91.1]). Discussion: We attribute the effectiveness of the rule-based method to the highly localized and formulaic language used in reporting of limitations in clinical research publications. Experiments with training size and composition show that more data does not necessarily lead to higher accuracy in the machine learning-based approaches. Conclusion: The approach presented can be incorporated into the workflows of stakeholders focusing on research transparency to improve reporting of limitations in clinical studies.

opencc-zeroDec 2017View details →

opencc-by-sa-4.0Mar 2016View details →

Clinical medical research use case evaluation of the VFramework

Context models, straces, etc. created for application of the VFramework.

opencc-by-4.0Apr 2016View details →

Figure 2 from: Klein A, Ghosh S (2016) Graph-based clinical diagnosis and prediction using multi-modal neuroimaging data. Research Ideas and Outcomes 2: e8835. https://doi.org/10.3897/rio.2.e8835

Figure 2 - Examples of automatically extracted features (MRI) (a) Example structural features (left lateral views of volumes, surfaces, curves, and points) (b) Schematic feature hierarchy: 3-D gyrii surround a 2-D sulcal ribbon with 1-D fundus containing 0-D pits

opencc-by-4.0Apr 2016View details →

Figure 1 from: Klein A, Ghosh S (2016) Graph-based clinical diagnosis and prediction using multi-modal neuroimaging data. Research Ideas and Outcomes 2: e8835. https://doi.org/10.3897/rio.2.e8835

Figure 1 - Examples of graph-based representations of scientific data among hundreds on the www.visualcomplexity.com website (categories on the site include biology, food webs and semantic, social, and knowledge networks). Lower left images of DTI, connectome, and network hubs are from Olaf Sporns (2010, Scholarpedia, 5(2):5584).

opencc-by-4.0Apr 2016View details →

Figure 3 from: Klein A, Ghosh S (2016) Graph-based clinical diagnosis and prediction using multi-modal neuroimaging data. Research Ideas and Outcomes 2: e8835. https://doi.org/10.3897/rio.2.e8835

Figure 3 - Example of a graph-based representation of MRI and DTI features (a) A gray/white matter surface (left lateral view) with (visible) sulcal pits highlighted. These features go by different names (sulcal roots, buried gyrii, annectant gyrii, plis de passage) and may be well conserved structures formed early in development. (b) DTI connectivity graph computed on the same patient with depression as on the left panel. Vertices represent automatically extracted sulcal pits and each edge indicates a connection probability greater than 0.01 between two vertices.

A Screening Study to Detect BRAF V600 Mutation-Positive Patients For Enrollment Into Clinical Research Studies of Zelboraf (Vemurafenib)

ClinicalTrials.gov study NCT01804140. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Clinical Research of S-1 Versus Pemetrexed in the Maintenance Treatment of Advanced NSNSCLC

ClinicalTrials.gov study NCT03700333. IPD Sharing: UNDECIDED. Countries: 0. Publications: 3.

Fluids and Catheters Treatment Trial (FACTT) - ARDS Clinical Research Network

ClinicalTrials.gov study NCT00281268. IPD Sharing: Not stated. Countries: 0. Publications: 4.

Research on the Clinical Effect of Effective Prescription in Treating Unstable Angina.

ClinicalTrials.gov study NCT03171597. IPD Sharing: UNDECIDED. Countries: 0. Publications: 5.

Clinical Research on the Efficacy of Acupuncture Treatment in Chronic Low Back Pain

ClinicalTrials.gov study NCT00815529. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Analyzing Clinical Research Participation of Patients With Spinal Cord Injury

ClinicalTrials.gov study NCT05831163. IPD Sharing: UNDECIDED. Countries: 0. Publications: 3.

Research on the Clinical Effect of Xuefu Zhuyu Decoction in Treating Unstable Angina.

ClinicalTrials.gov study NCT03179618. IPD Sharing: UNDECIDED. Countries: 0. Publications: 5.

controlledIPD-YESFeb 2026View details →

Chronic Prostatitis Collaborative Research Network Clinical Trial- Ciprofloxacin and Tamsulosin

ClinicalTrials.gov study NCT04552431. IPD Sharing: YES. Countries: 0. Publications: 1.

closedIPD-NOFeb 2026View details →

SmartMouth Advanced Clinical Formula Clinical Research Design Protocol

ClinicalTrials.gov study NCT02709785. IPD Sharing: NO. Countries: 1. Publications: 0.

A Clinical Research Studying a Method of Intervention for Children Diagnosed With Anxiety Disorder: Attentional Bias Intervention

ClinicalTrials.gov study NCT00482820. IPD Sharing: Not stated. Countries: 0. Publications: 1.