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222 results for “clinical standard”
The Comparative Efficacy of Standard Treatment Plus Ribavirin vs Standard Treatment Alone in Preventing Clinically Significant Hemorrhage in Patients With Dengue Fever
ClinicalTrials.gov study NCT06744777. IPD Sharing: YES. Countries: 1. Publications: 7.
A Clinical Study to Test How Effective and Safe GLPG1690 is for Participants With Idiopathic Pulmonary Fibrosis (IPF) When Used Together With Standard of Care
ClinicalTrials.gov study NCT03733444. IPD Sharing: NO. Countries: 15. Publications: 4.
Detection of Clinically Significant Prostate Cancer Using Transperineal Targeted Biopsy Compared to Standard Transrectal Biopsy
ClinicalTrials.gov study NCT03044197. IPD Sharing: NO. Countries: 1. Publications: 1.
Surgery and/or Radiation Therapy or Standard Therapy and/or Clinical Observation in Treating Patients With Previously Treated Stage IV Non-small Cell Lung Cancer
ClinicalTrials.gov study NCT01725165. IPD Sharing: Not stated. Countries: 2. Publications: 2.
A Clinical Study to Test How Effective and Safe GLPG1690 is for Subjects With Idiopathic Pulmonary Fibrosis (IPF) When Used Together With Standard of Care
ClinicalTrials.gov study NCT03711162. IPD Sharing: NO. Countries: 15. Publications: 3.
Clinical Evaluation of the Performance of Bionet Sphygmomanometer Modules Based on Protocol Presented by the International Organization for Standardization
ClinicalTrials.gov study NCT06609486. IPD Sharing: NO. Countries: 1. Publications: 4.
Study of Early Endocapsule (EC) in Clinical Decision Unit Versus Standard of Care Work-up for GI Bleeding
ClinicalTrials.gov study NCT03955055. IPD Sharing: NO. Countries: 1. Publications: 2.
CodiEsp Silver Standard: Participant predictions in eHealth CLEF2020 - Spanish clinical cases coded in ICD10 (CIE10)
<p><strong>Introduction</strong></p> <p>Predictions in the background set of <a href="https://temu.bsc.es/codiesp/">eHealth CLEF 2020 Task 1</a> participants.</p> <p> </p> <p><strong>Zip structure</strong></p> <p>One directory per CodiEsp subtask. Within each CodiEsp subtask directory, there is one directory per team that contains the prediction runs.</p> <p> </p> <p><strong>Format</strong><br> The text documents are distributed in plain text files, UTF-8 encoding.<br> The CodiEsp Silver Standard annotations have the following format:</p> <p>For the sub-tracks CodiEsp-Diagnostic and CodiEsp-Procedure, the file files have the following fields:</p> <pre>articleID ICD10-code </pre> <p>Tab-separated files for the sub-track CodiEsp-X (explainability) contain extra fields that provide the text-reference and its position:</p> <pre>articleID label ICD10-code text-reference reference-position</pre> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/codiesp/">Web</a></strong></li> <li><strong>Citation: </strong>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</li> <li><strong><a href="https://zenodo.org/record/3837305#.X7T9KVlKg5k">Gold Standard corpus</a></strong></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3878178">Annotation guidelines</a></strong></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhC24g5dsp5eVMp8BZFWCraX"><strong>YouTube presentations</strong></a></li> <li><a href="https://temu.bsc.es/cantemist/?p=4606"><strong>Participant codes</strong></a></li> </ul> <p> </p> <p>All credit to CodiEsp participants</p>
Cantemist corpus: gold standard of oncology clinical cases annotated with CIE-O 3 terminology
<p><strong>Intro:</strong></p> <p>Cantemist shared task dataset (divided in train, dev1, dev2 and test). In addition, we include here the Cantemist background set.</p> <p>It contains the train, development and test sets of the three subtasks: cantemist-ner, cantemist-norm and cantemist-coding with Gold Standard annotations.</p> <p>In addition, it contains the documents of the background set, without annotations.</p> <p> </p> <p><strong>Please cite if you use this dataset:</strong></p> <p>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</p> <pre><code>@inproceedings{miranda2020named, title={Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results}, author={Miranda-Escalada, A and Farr{\'e}, E and Krallinger, M}, booktitle={Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings}, year={2020} }</code></pre> <p> </p> <p><strong>Format:</strong></p> <p>For subtasks cantemist-norm and cantemist-ner, annotations are distributed in Brat format. See <a href="https://brat.nlplab.org/standoff.html">Brat webpage</a> for more information</p> <p>For subtask cantemist-coding, codes are grouped in a TSV file with the following columns (this follows the format used in <a href="https://temu.bsc.es/codiesp/">CodiEsp shared task</a>): </p> <blockquote> <p>filename code</p> </blockquote> <p> </p> <p><strong>Shared task goal:</strong></p> <p>In the three subtasks, the goal will be to predict the annotations (either the ANN files or the TSV with the codes) given only the plain text files. </p> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/cantemist/">Web</a></strong></li> <li><strong><a href="http://ceur-ws.org/Vol-2664/cantemist_overview.pdf">Citation</a>: </strong>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</li> <li><strong><a href="https://doi.org/10.5281/zenodo.4010899">Silver Standard corpus</a></strong></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3878178">Annotation guidelines</a></strong></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhC24g5dsp5eVMp8BZFWCraX"><strong>YouTube presentations</strong></a></li> <li><a href="https://temu.bsc.es/cantemist/?p=4606"><strong>Participant codes</strong></a></li> </ul> <p> </p> <p>For further information, please visit <a href="https://temu.bsc.es/cantemist/">https://temu.bsc.es/cantemist/</a> or email us at encargo-pln-life@bsc.es</p>
Cantemist Silver Standard: Participant predictions in SEPLN IberLEF2020 - Spanish oncology clinical cases coded in ICD-O
<p><strong>Introduction</strong></p> <p>Predictions in the background set of Cantemist participants.</p> <p> </p> <p><strong>Zip structure</strong></p> <p>One directory per Cantemist subtask. Within each Cantemist subtask directory, there is one directory per team that contains the prediction runs.</p> <p> </p> <p><strong>Format</strong></p> <p>The text documents are distributed in plain text files, UTF-8 encoding.<br> The CodiEsp Silver Standard annotations have the following format:</p> <p>For the sub-tracks Cantemist-NER and Cantemist-Norm, the files are in Brat format.</p> <p>For the sub-track Cantemist-Coding files have the following fields:</p> <pre>articleID ICDO-code </pre> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/cantemist/">Web</a></strong></li> <li><strong>Citation: </strong>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</li> <li><a href="https://doi.org/10.5281/zenodo.3773228"><strong>Gold Standard corpus</strong></a></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3878178">Annotation guidelines</a></strong></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhC24g5dsp5eVMp8BZFWCraX"><strong>YouTube presentations</strong></a></li> <li><a href="https://temu.bsc.es/cantemist/?p=4606"><strong>Participant codes</strong></a></li> </ul> <p> </p> <p>All credit to Cantemist participants. </p> <p> </p> <p>For more information, visit the track webpage: <a href="http://temu.bsc.es/cantemist/">http://temu.bsc.es/cantemist/</a> or email us at encargo-pln-life@bsc.es</p>
Implementing standardized provider documentation in a tertiary epilepsy clinic: supplemental material
<p class="CxSpFirst"><b>Objective: </b>To incorporate standardized documentation into an epilepsy clinic and use these standardized data to compare patients' perception of epilepsy diagnosis to provider documentation.</p> <p class="CxSpMiddle"><b>Methods: </b>Using quality improvement methodology, we implemented interventions to increase documentation of epilepsy diagnosis, seizure frequency and type from 49.8% to 70% of adult non-employee patients seen by six providers over five months of routine clinical care. The main intervention consisted of an interactive SmartPhrase that mirrored a documentation template developed by the Epilepsy Learning Healthcare System. We assessed the weekly proportion of complete SmartPhrases among eligible patient-encounters with a statistical process control chart. We used a subset of patients with established epilepsy care linked to existing patient-reported survey data to examine the proportion of patient-to-provider agreement on epilepsy diagnosis (yes vs no/unsure). We also examined socio-demographic and clinical characteristics of patients who disagreed vs agreed with provider's documentation of epilepsy diagnosis.</p> <p><b>Results: </b>The median SmartPhrase weekly completion rate was 78%. Established patients disagreed with providers with respect to epilepsy diagnosis in 18.5% of encounters (κ = 0.13), indicating that they did not have, or were unsure if they had, epilepsy despite having a provider-documented epilepsy diagnosis. Patients who disagreed with providers were similar to those who agreed with respect to age, sex, ethnicity, marital status, seizure frequency, type, and other quality of life measures.</p> <p class="CxSpMiddle"><b>Conclusions: </b>This project supports the feasibility of implementing standardized documentation of data relevant to epilepsy care in a tertiary epilepsy clinic and highlights an opportunity for improvement in patient-provider communication.</p>
SympTEMIST Corpus: Gold Standard annotations for clinical symptoms, signs and findings information extraction
<p><strong>SympTEMIST</strong> stands for Symptoms TExt MIning Shared Task. It is a shared task and set of resources focused on the <strong>detection of mentions, normalization and indexing of symptoms, signs and findings in medical documents</strong> in <strong>Spanish</strong>. SympTEMIST is complementary to the DisTEMIST (https://temu.bsc.es/distemist) and MedProcNER/ProcTEMIST (https://temu.bsc.es/medprocner) corpora as they all use the same document collection.</p> <p> </p> <p><strong>Please cite if you use this dataset:</strong></p> <p>"Lima-López, S., Farré-Maduell, E., Gasco-Sánchez, L., Rodríguez-Miret, J. and Krallinger, M. (2023). Overview of SympTEMIST at BioCreative VIII: corpus, guidelines and evaluation of systems for the detection and normalization of symptoms, signs and findings from text. In: <em>Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models</em>."</p> <p> </p> <p>This repository includes the:</p> <ul> <li><strong>Train </strong>and<strong> Test Set </strong>for the three subtasks</li> <li><strong>SYMPTEMIST gazetteer </strong>of <strong>SNOMED symptoms, signs & findings </strong></li> <li><strong>Multilingual </strong>Silver Standard<strong> </strong>in <strong>9 languages:</strong> <ul> <li><em><strong>English</strong></em></li> <li><em><strong>Portuguese</strong></em></li> <li><em><strong>French</strong></em></li> <li><em><strong>Italian</strong></em></li> <li><em><strong>Romanian</strong></em></li> <li><em><strong>Catalan</strong></em></li> <li><em><strong>Swedish</strong></em></li> <li><em><strong>Dutch</strong></em></li> <li><em><strong>Czech</strong></em></li> </ul> </li> <li><strong>Background set</strong> of over 15,000 clinical cases.</li> <li><strong>Spanish</strong> Silver Standard (predictions by participants over the background set)</li> </ul> <p> </p> <p>Please read the README file attached for more information on folder structure and file format.</p> <p> </p> <p>SympTEMIST was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of BioCREATIVE 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/symptemist.</p> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><a href="https://temu.bsc.es/symptemist/"><strong>Task web</strong></a></li> <li><a href="https://biocreative.bioinformatics.udel.edu/"><strong>BioCreative web</strong></a></li> <li><strong>Citation: </strong>Lima-López, S., Farré-Maduell, E., Gasco-Sánchez, L., Rodríguez-Miret, J. and Krallinger, M. (2023). Overview of SympTEMIST at BioCreative VIII: corpus, guidelines and evaluation of systems for the detection and normalization of symptoms, signs and findings from text. In: <em>Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models</em>.</li> <li><a href="../records/8246440"><strong>Annotation guidelines</strong></a></li> <li><a href="../records/10103191"><strong>BioCreative/AMIA workshop proceedings</strong></a></li> <li><a href="../doi/10.5281/zenodo.10104546"><strong>Overview paper</strong></a></li> <li><a href="https://www.youtube.com/playlist?list=PLyLDDulunoofv79Pci0-y2rGP5GOBoZhp"><strong>Youtube videos (overview & teams)</strong></a></li> <li><a href="https://www.slideshare.net/MartinKrallinger/symptemist-shared-task-on-symptoms-signs-and-findings-detection-and-normalization-task-overview-talk-at-biocreative-viii-workshop-amia-2023"><strong>SympTEMIST overview talk slides at BioCreative/AMIA workshop</strong></a></li> </ul> <p> </p> <p><strong>License</strong></p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p><strong>Contact</strong></p> <p>If you have any questions or suggestions, please contact us at:</p> <p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <p><strong>Additional resources and corpora</strong></p> <p>If you are interested in SympTEMIST, you might want to check out these corpora and resources:</p> <ul> <li><a href="../records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT, same document collection)</li> <li><a href="../records/8224056">MedProcNER </a>(Corpus of clinical procedure mentions and normalization to SNOMED CT, same document collection)</li> <li><a href="../records/4270158">PharmaCoNER</a> (Corpus of medications, drugs, chemical substances, genes, proteins and vaccine mentions and normalization, same document collection)</li> <li><a href="../records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)</li> <li><a href="../records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, different document collection with some overlapping documents)</li> <li><a href="../records/4279323">MEDDOCAN</a> (Corpus of mentions of Personal Health Identifiers (PHI), modified synthetic verions of the document collection)</li> <li><a href="../records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li> <li><a href="../records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection)</li> <li><a href="../records/7684093">LivingNER</a> (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy, different document collection with some overlapping documents)</li> <li><a href="../records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection)</li> <li><a href="../records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries), same document collection)</li> <li><a href="../records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection)</li> <li><a href="../records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li> </ul> <p> </p>
Clinical Outcomes of Low Dose PK-guided EHL FVIII Concentrates Versus Standard Prophylaxis in Severe Haemophilia A
ClinicalTrials.gov study NCT05281185. IPD Sharing: YES. Countries: 1. Publications: 1.
Clinical Trial of Integrated Treatment Versus Standard Treatment in First Episode Psychosis
ClinicalTrials.gov study NCT00157313. IPD Sharing: Not stated. Countries: 1. Publications: 11.
Evaluation of a Standard Epidural Catheter During Clinical Practice
ClinicalTrials.gov study NCT00396903. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Clinical Study of Blood Pressure Monitor in Conformance With the ANSI/AAMI SP10 Standard
ClinicalTrials.gov study NCT01295528. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Clinical Study Evaluating Effects of Pharmacogenetic-guided vs Standard-of-Care Treatment on Depression and/or Anxiety
ClinicalTrials.gov study NCT02878928. IPD Sharing: NO. Countries: 1. Publications: 1.
Reduced-dose vs Standard-dose Irradiation for Low-risk Clinical Target Volume in Nasopharyngeal Carcinoma.
ClinicalTrials.gov study NCT07201337. IPD Sharing: NO. Countries: 1. Publications: 5.
Empathy and Standard Diagnostic Procedures in an Outpatient Breast Clinic Might Not be Enough
ClinicalTrials.gov study NCT02796612. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
A Clinical Study To Measure The Effect Of Use Of Artificial Intelligence (AI) Enabled Computer Aided Detection (CADe) Assistance Software In Detecting Colon Polyps During Standard Colonoscopy Procedur
ClinicalTrials.gov study NCT04555135. IPD Sharing: NO. Countries: 1. Publications: 10.
ScienceDex guides
Understand access before you commit
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.