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Dataset results
939 results for “Validation studies”
Validation Study of ClassIntra®
ClinicalTrials.gov study NCT03009929. IPD Sharing: Not stated. Countries: 12. Publications: 2.
Prospective Validation Study of AI-based Prediction Algorithm for the Prediction of Paroxysmal Atrial Fibrillation
ClinicalTrials.gov study NCT05725187. IPD Sharing: NO. Countries: 1. Publications: 5.
A Validation Study to Evaluate the Performance of Caption Health Lung Guidance and Interpretation
ClinicalTrials.gov study NCT05992324. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Development and validation of a postoperative delirium prediction model for patients admitted to an intensive care unit in China: a prospective study
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Genotyping validates the efficacy of photographic identification in a capture-mark-recapture study based on the head scale patterns of the prairie lizard (Sceloporus consobrinus)
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Data from: Tree ring δ15N as validation of space-for-time substitution in disturbance studies of forest nitrogen status
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Data from: Development and field validation of a regional, management-scale habitat model: a koala Phascolarctos cinereus case study
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Data from: Validation of the hospital frailty risk score in a tertiary care hospital in Switzerland: results of a prospective, observational study
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Data from: Comparative study of the validity of three regions of 18S-rRNA gene for massively parallel sequencing-based monitoring of the planktonic eukaryote community
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Data from: A from-benchtop-to-desktop workflow for validating HTS data and for taxonomic identification in diet metabarcoding studies
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Nerve ultrasound for diagnosing chronic inflammatory neuropathy: a multicenter validation study
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Development and validation of a postoperative delirium prediction model for patients admitted to an intensive care unit in China: a prospective study
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Data from: Simulation-based validation of spatial capture-recapture models: a case study using mountain lions
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Criteria for defining interictal epileptiform discharges in EEG: a clinical validation study
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Experimental and Simulation Results of "Hemodynamic study in 3D printed stenotic coronary artery models: experimental validation and transient simulation"
<p>This repository contains the experimental and simulation results of the submitted article "Hemodynamic study in 3D printed stenotic coronary artery models: experimental validation and transient simulation" by Carvalho, V., Rodrigues, N., Ribeiro, R., Costa, P., Lima, R., Teixeira, S. </p>
Data from: Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation
<p class="AbstractSummary"><b>Background</b></p> <p class="AbstractSummary">Prostate cancer (PC) is the most frequently diagnosed cancer in North American men. Pathologists are in critical need of accurate biomarkers to characterize PC, particularly to confirm the presence of intraductal carcinoma of the prostate (IDC-P), an aggressive histopathological variant for which therapeutic options are now available. Our aim was to identify IDC-P with Raman micro-spectroscopy and machine learning technology following a protocol suitable for routine clinical histopathology laboratories.</p> <p class="AbstractSummary"><b>Methods and findings</b></p> <p class="AbstractSummary">We used Raman micro-spectroscopy to differentiate IDC-P from PC, as well as PC and IDC-P from benign tissue on formalin-fixed paraffin-embedded first-line radical prostatectomy specimens (embedded in tissue microarrays, TMAs) from 483 patients treated in three Canadian institutions between 1993 and 2013. The main measures were the presence or absence of IDC-P and of PC, regardless of the clinical outcomes. Most of the 483 patients were pT2 stage (44–69%), and pT3a (22–49%) was more frequent than pT3b (9–12%). After approval of the construction of the TMAs by local ethics review board, the diagnostic accuracy study was approved by the Centre hospitalier de l'Université de Montréal (CHUM) ethics review board. Briefly, two consecutive sections of each TMA block were cut. The first section was transferred onto a glass slide to perform immunohistochemistry with H&E counterstaining for cell identification. The second section was placed on an aluminum slide, dewaxed, and then used to acquire an average of 7 Raman spectra per specimen (between 4 and 24 Raman spectra, 4 acquisitions / TMA core). Raman spectra of each cell type were then analyzed to retrieve tissue-specific molecular information and to generate classification models using machine learning technology. <span>Models were trained and cross-validated using data from one institution. Accuracy, sensitivity and specificity were respectively of 87 ± 5%, 86 ± 6% and 89 ± 8% to differentiate PC from benign tissue, and of 95 ± 2%, 96 ± 4% and 94 ± 2% respectively to differentiate IDC-P from PC. The trained models were then tested on data from two independent institutions, reaching accuracies, sensitivities and specificities of 84 and 86%, 84 and 87%, and 81 and 82%, respectively</span><span> to diagnose PC, and of 85 and 91%, 85 and 88%, and 86 and 93% respectively for the identification of IDC-P.</span> IDC-P could further be differentiated from high-grade prostatic intraepithelial neoplasia (HGPIN), a pre-malignant intraductal proliferation which can be mistaken as IDC-P, with accuracies, sensitivities and specificities >95% in both training and testing cohorts. As we used stringent criteria to diagnose IDC-P, the main limitation of our study is the exclusion of borderline, difficult to classify lesions from our datasets.</p> <p class="AbstractSummary"><b>Conclusions</b></p> <p>In this study, we developed classification models for the analysis of Raman micro-spectroscopy data to differentiate IDC-P, PC and benign tissue, including HGPIN. Raman micro-spectroscopy could be a next-generation histopathological technique used to <span>reinforce the identification of high-risk PC patients and lead to more precise diagnosis of IDC-P.</span></p>
Reproducible Validation and Replication Studies in Nanoscale Physics (problem datasets for validation and replications from Ellis et al., 2016)
<p>Problem folders including all the input files necessary to reproduce the computations of the results related to Validation and replication of Ellis et al. 2016, on the paper: Reproducible Validation and Replication Studies in Nanoscale Physics</p>
Reproducible Validation and Replication Studies in Nanoscale Physics (repro results plots - Rockstuhl et al., 2005)
<p>This archive contains the Jupyter notebooks needed to reproduce the figures of the paper that are related to the replication of Rockstuhl et al. 2005. For further information direct to the README.md file. </p>
Figure 2 from: Logoyda L (2020) Efficient validated method of HPLC to determine amlodipine in combinated dosage form containing amlodipine, enalapril and bisoprolol and in vitro dissolution studies with in vitro/ in vivo correlation. Pharmacia 67(2): 55-61. https://doi.org/10.3897/pharmacia.67.e48220
Figure 2 Representative chromatogram of amlodipine in combinated tablets (1- peak of bisoprolol, 2- peak of enalapril, 3 – peak of amlodipine).
Data from: Comparison of the validity of the checklist assessment in the cardiac arrest simulations with the app in an academic hospital in Taiwan: a retrospective observational study
Background: Robust assessment is a crucial component in Advanced Cardiac Life Support (ACLS) training to determine whether participants have achieved learning objectives with little or no variation in their overall outcomes. This study aimed to evaluate resuscitation performance by real-time logs. We hypothesized that instructors may not be able to evaluate time-sensitive parameters, namely, chest compression fraction, time to initiating chest compression, and time to initiating defibrillation efficiently in a subjective manner. Methods: Video records and formal checklist-based test results of Megacode scenarios for the ACLS certification examination at several hospitals in Taipei were examined. For the study interest, three time-sensitive parameters were measured via video review assisted by a mobile phone application, and were used for evaluation. We evaluated if the pass/fail results made by instructors via checklists were correlated with these parameters. Results: A total of 185 Megacode scenarios were eligible for the final analysis. Among the three parameters, good chest compression fraction was statistically significant with higher odds ratio (OR) of passing (OR = 3.65; 95% confidence interval [CI]: 1.36-9.91; P = 0.01). In 112 participants with one parameter that did not meet the criteria, 25 were graded as fail, making the specificity 22.3% (95% CI: 15.0-31.2%). Conclusions: Visual observation of cardiopulmonary resuscitation performance is not accurate when evaluating time-sensitive parameters. Objective results should be offered for training outcome evaluation, and also for feedback to participants.
ScienceDex guides
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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.