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1,264 results for “clinical assessment”
DATA SET: Performance Assessment of a Commercial Continuous-Wave Near-Infrared Spectroscopy Tissue Oximeter for Suitability for Use in an International, Multi-Center Clinical Trial
<p>This repository contains the data sets related to the publication:</p> <p>Cortese, L.; Zanoletti, M.; Karadeniz, U.; Pagliazzi, M.; Yaqub, M.A.; Busch, D.R.; Mesquida, J.; Durduran, T. Performance Assessment of a Commercial Continuous-Wave Near-Infrared Spectroscopy Tissue Oximeter for Suitability for Use in an International, Multi-Center Clinical Trial. <em>Sensors</em> <strong>2021</strong>, <em>21</em>, 6957. https://doi.org/10.3390/s21216957</p>
Dataset related to article "Harmonization of sensorimotor deficit assessment in a registered multicentre pre-clinical randomized controlled trial using two models of ischemic stroke"
<p>https://figshare.com/search?q=10.6084%2Fm9.figshare.21346731</p>
A Clinical Study to Assess the Efficacy and Safety of Gene Therapy for the Treatment of Cerebral Adrenoleukodystrophy (CALD)
ClinicalTrials.gov study NCT03852498. IPD Sharing: YES. Countries: 6. Publications: 1.
Clinical Trial to Assess the Effectiveness of Applying Dry Local Heat and/ or High Tourniquet Pressure for Venipuncture.
ClinicalTrials.gov study NCT04027218. IPD Sharing: YES. Countries: 1. Publications: 8.
Longitudinal Outcome in a Veterans Geriatric Multifactorial Falls Assessment Clinic
ClinicalTrials.gov study NCT02356211. IPD Sharing: NO. Countries: 1. Publications: 5.
Clinical trial generalizability assessment in the big data era: a review
<p><span><span><span>Clinical studies, especially randomized controlled trials, are essential for generating evidence for clinical practice. However, generalizability is a long-standing concern when applying trial results to real-world patients. Generalizability assessment is thus important, nevertheless, not consistently practiced. We performed a systematic scoping review to understand the practice of generalizability assessment. We identified 187 relevant papers and systematically organized these studies in a taxonomy with three dimensions: (1) data availability (i.e., before or after trial [<i>a priori</i> vs <i>a posteriori</i> generalizability]), (2) result outputs (i.e., score vs non-score), and (3) populations of interest. We further reported disease areas, underrepresented subgroups, and types of data used to profile target populations. We observed an increasing trend of generalizability assessments, but less than 30% of studies reported positive generalizability results. As <i>a priori</i> generalizability can be assessed using only study design information (primarily eligibility criteria), it gives investigators a golden opportunity to adjust the study design before the trial starts. Nevertheless, less than 40% of the studies in our review assessed <i>a priori</i> generalizability. With the wide adoption of electronic health records systems, rich real-world patient databases are increasingly available for generalizability assessment; however, informatics tools are lacking to support the adoption of generalizability assessment practice.</span></span></span></p>
CONSORT flow diagram for The assessment of educational and supportive care to the infertile females undergoes In Vitro Fertilization procedure by clinical pharmacist: a randomized clinical trial
<p><strong>The assessment of educational and supportive care to the infertile females undergoes In Vitro Fertilization procedure by </strong>a <strong>clinical pharmacist: a randomized clinical trial</strong>.</p>
Assessment of Self-Medication Practices and Safety Profile of Medicines Utilisation among Pregnant Women attending Antenatal Clinics in Freetown, Sierra Leone: A Multicentre Cross-Sectional Descriptive Study
<p><strong><span>Background: </span></strong><span>Despite the potential fetal and maternal risks of self-medication, studies on self-medication practice and the safety profile of medicines used during pregnancy are scarce in our setting. This study determined the self-medication practice and safety profile of medicines used among pregnant women.</span></p> <p><strong><span>Methods: </span></strong><span>This cross-sectional study was conducted in face-to-face interviews among 345 pregnant women at three hospitals in Sierra Leone. Data were analyzed using descriptive statistics and binary logistic regression to <span>determine the prevalence and associated factors of self-medication. </span></span></p> <p><strong><span>Results: </span></strong><span>A total of 345 pregnant women participated in the study. The prevalence of self-medication among pregnant women with conventional and/or herbal medicine was </span><span>132 (38.3%)</span><span>. Also, 93 (75%) of the conventional medicines (CMs) were categorized as probably safe, of which paracetamol 36 (29.0%) was commonly used, followed by amoxicillin 23 (18.5%) and antimalarials 22 (17.7%) for common illnesses such as </span><span>headache 30 (25.4%), </span><span>urinary tract infection </span><span>23 (19.4%)<span> and malaria 22 (18.6%). The common reason for self-medication was previous experience with the disease 24 (27.3%). </span><em>Luffa acutangula</em> 19 (30.2%) <span>was the most used herbal medicine (HM), </span>and Oedema 30 (47.6%) was the most reported ailment. <span>Among the HM users,</span> 34 (54.0%)<span> believe they are more effective than CMs. Secondary school education (AOR = 2.128, 95%CI = 1.191 – 3.804, p = 0.011), tertiary education (AOR = 2.915, 95%CI = 1.104 – 7.693, p = 0.031), monthly income of greater than NLe 1,000 (AOR = 4.084, 95% CI = 1.269 – 13.144, p = 0.018), and perceived maternal illness (AOR = 0.367, CI = 0.213 – 0.632, p = <0.001) were predictors of self-medication.</span></span></p> <p><strong><span>Conclusion: </span></strong><span>Self-medication practice was highly prevalent and was associated with educational status, monthly income, and maternal illness during pregnancy. Therefore, intervention programs should be designed and implemented to minimize the practice and risk associated with self-medication among pregnant women.</span></p>
A systematic assessment of deep learning methods for drug response prediction: from in-vitro to clinical application
<p>https://github.com/LihongLab/Suppl-data-Benchmark</p> <p>## GDSC dataset</p> <p>**Table S3.** GDSC gene expression profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**Table S4.** GDSC gene mutation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**Table S5.** GDSC copy number variation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**Table S6.** GDSC drug response data for 966 cancer cell lines and 282 drugs in the form of the natural logarithm of the IC50 readout. The first column shows the cell line name and tissue collection site, the second column shows the drug name, and the third column shows the drug response readout.</p> <p> </p> <p>**Table S7.** GDSC annotations for 282 drugs include drug name, PubChem CID, PubChem canonical SMILES, Rdkit canonical SMILES, Target Pathway, standard deviation, bimodality coefficient and density coverage.</p> <p>## TCGA dataset</p> <p>**Table S8.** TCGA gene expression profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**Table S9.** TCGA gene mutation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**Table S10.** TCGA copy number variation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**Table S11.** TCGA clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, the fourth column shows the cancer type, and the last column shows the clinical label as responder or non-responder.</p>
Assessing the feasibility and acceptability of a pre-clinic vital signs assessment in primary care: a pilot study.
Open the record for dataset details and reuse information.
A systematic assessment of deep learning methods for drug response prediction: From in vitro to clinical applications
<p>## GDSC dataset</p> <p>**GDSC_EXP.csv** GDSC gene expression profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**GDSC_MUT.csv** GDSC gene mutation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**GDSC_CNV.csv** GDSC copy number variation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**GDSC_DR.csv** GDSC drug response data for 966 cancer cell lines and 282 drugs in the form of the natural logarithm of the IC50 readout. The first column shows the cell line name and tissue collection site, the second column shows the drug name, and the third column shows the drug response readout.</p> <p> </p> <p>**GDSC_DrugAnnotation.csv** GDSC annotations for 282 drugs include drug name, PubChem CID, PubChem canonical SMILES, Rdkit canonical SMILES, Target Pathway, standard deviation, bimodality coefficient and density coverage.</p> <p>## TCGA dataset</p> <p>**TCGA_EXP.csv** TCGA gene expression profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**TCGA_MUT.csv** TCGA gene mutation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**TCGA_CNV.csv** TCGA copy number variation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**TCGA_DR.csv** TCGA clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, the fourth column shows the cancer type, and the last column shows the clinical label as responder or non-responder.</p> <p>## PMID17185464 (Bortezomib) dataset</p> <p>**PMID17185464_EXP.csv** Bortezomib clinical trial gene expression profiles, where each column represents a patient in the form of patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p>**PMID17185464_DR.csv** Bortezomib clinical trial clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, and the last column shows the clinical label as responder or non-responder (NR: Non-responder, R: Responder).</p>
The PLATINUM Clinical Trial to Assess the PROMUS Element Stent System for Treatment of Long De Novo Coronary Artery Lesions (PLATINUM LL)
ClinicalTrials.gov study NCT01500434. IPD Sharing: UNDECIDED. Countries: 7. Publications: 2.
Study to Assess the Clinical Efficacy and Safety of Intravitreal Aflibercept Injection (IAI;EYLEA®;BAY86-5321) in Patients With Branch Retinal Vein Occlusion (BRVO)
ClinicalTrials.gov study NCT01521559. IPD Sharing: Not stated. Countries: 3. Publications: 1.
A Clinical Study Assessing Critical Errors, Training/Teaching Time, and Preference Attributes of the ELLIPTA® Dry Powder Inhaler, in Comparison to Combinations of Dry Powder Inhalers Used to Provide T
ClinicalTrials.gov study NCT02982187. IPD Sharing: NO. Countries: 2. Publications: 1.
A Multi-Center, Randomized, Double Masked, Parallel-Group, Vehicle-Controlled, Clinical Study to Assess the Safety and Efficacy of Reproxalap Ophthalmic Solution in Subjects With Dry Eye Disease
ClinicalTrials.gov study NCT03404115. IPD Sharing: NO. Countries: 1. Publications: 1.
Clinical Trial to Assess the Efficacy and Safety of Ciclesonide Hydrofluoroalkane (HFA) Nasal Aerosol for the Treatment of Seasonal Allergic Rhinitis
ClinicalTrials.gov study NCT00790023. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Assessing Symptomatic Clinical Episodes in Depression
ClinicalTrials.gov study NCT03595579. IPD Sharing: YES. Countries: 1. Publications: 1.
A Study to Assess Safety/Tolerability, pk, Effects on Histology, Clinical Parameters of Givinostat in Children With DMD
ClinicalTrials.gov study NCT01761292. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Clinical Assessment of a Customized Free-form Progressive Addition Lens Spectacle
ClinicalTrials.gov study NCT01234207. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Clinical Study to Assess the Efficacy and Safety of G238 Compared to Clotrimazole Otic Solution in the Treatment of Otomycosis
ClinicalTrials.gov study NCT01993823. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.