Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
251
datasets available to search
ShareScore release 0.9.0
Dataset results
251 results for “triage”
Data from: A risk stratification tool for prehospital triage of patients exposed to a whiplash trauma
Objective: Our aim was to develop a risk stratification model to predict the presence of a potentially more sinister injury in patients exposed to a whiplash trauma. Methods: The study base comprised of 3,115 residents who first sought healthcare contact within one week after being exposed to a whiplash trauma between 1999-2008, from within a defined geographical area, Skaraborg County in south-western Sweden. Information about gender, age, time elapsed prior to seeking care, type of health care contact, and hospitalization was retrieved. Eighteen potential risk factors were identified and evaluated using multivariate logistic regression. Results: Of 3,115 patients, 215 (6.9%) required hospital admission so theoretically 93% could have been initially assessed by primary health care. However, only 46% had their first contact in primary health care. All patients had symptoms resulting in a diagnosis of whiplash injury. Four risk factors were found to be associated with hospital admission: commotio cerebri (OR 31, 19-51), fracture / luxation (OR 11, 5.1-22), serious injury (OR 41, 8.0-210), and the patient sought care during the same day as the trauma (OR 5.9, 3.7- 9.5). These four risk factors explained 27 % of the variation for hospital admission and the area under curve (AUC) was 0.77 (0.74-0.80). Ninety-six percent of patients (2,985) had only a whiplash injury with none of the other four risk factors. These could be split into those attending health care the same day as the trauma, 1,737 (56%) with a 7.1% risk for hospital admission, and those attending health care later, 1,248 (40%) with a 1.3% risk for hospital admission. Conclusion: Patients with no signs of commotio cerebri, no fracture/luxation injury, no serious injury, comprising 96% of all patients exposed to a whiplash trauma can initially be referred to primary health care for initial assessment. However, those contacting the health care the same day as the trauma should be referred to a hospital for evaluation if they can't get an appointment with a general practitioner the same day.
Emergency Medicine: Life-Saving Strategies and Techniques - Prehospital Emergency Care, Triage Systems, Advanced Card Apologies for the premature response.
<p><span>Emergency medicine is a critical field that plays a pivotal role in saving lives and providing essential care during life-threatening situations. This paper aims to explore the key components of emergency medicine, with a focus on prehospital emergency care, triage systems, and advanced cardiac apologies. Through an in-depth analysis of current practices, strategies, and techniques, this paper provides valuable insights into the life-saving measures employed in emergency medicine.</span></p>
Search Strategies for Teledermatology for triage of primary care referrals: a health technology assessment
<p>The dataset includes the complete, reproducible search strategies for all bibliographic databases searched during this project. The search strategies were designed to answer the following research question: What is the clinical efficacy, effectiveness and safety of teledermatology fortriage from primary care referrals compared to traditional face-to-face dermatology consultations?</p>
Data set supplementing "A Use-Case Specific Framework for Designing Representative Vignettes (RepVig) and Evaluating Triage Accuracy of Laypeople and Symptom-Assessment Applications"
<p>This is the de-identified data set used to conduct the analyses of our study "A Use-Case Specific Framework for Designing Representative Vignettes (RepVig) and Evaluating Triage Accuracy of Laypeople and Symptom-Assessment Applications" (<a href="https://doi.org/10.1101/2024.04.02.24305193">https://doi.org/10.1101/2024.04.02.24305193</a>). The data comprises the answers to cases given by laypeople, symptom-assessment applications, and large language models and the corresponding solutions for each case. The cases were developed in the study with a focus on external validity.</p> <p>The dataset contains three datafiles: collected data for laypeople, for symptom-assessment applications, and for large language models. </p>
yaleemmlc/admissionprediction: Predicting hospital admission at emergency department triage using machine learning - Data and Scripts
<p>First release for PLOS One. Please cite original paper for all research using this dataset.</p>
Hackathon - TF-TG literature triage additional data
<p>TF = Transcription Factor, TG= target Gene</p> <p>Jamboree: manual triage/TF-TG relation annotation exercise</p> <p>Hackathon: automatic triage/TF-TG relation annotation exercise</p> <p>This link contains three files:</p> <ul> <li> <p>Exp_methods: A list of experimental methods used in the study of transcription factor and target gene interactions. These method names might be useful as semantic features or to index records characterizing experimentally TF-TG relations.</p> <ul> <li> <p>Name: <a href="https://zenodo.org/record/2562967/files/GREEKC_Hackathon_Exp_methods.zip?download=1">GREEKC_Hackathon_Exp_methods.zip</a></p> </li> <li> <p>Example: TRE_EM_prl_2 Electric Mobility Shift</p> </li> <li> <p>Format: tsv-separated columns (method Id, method name/alias)</p> </li> </ul> </li> <li> <p>Hs_tf_hgnc_ncbigeneid: A list of human transcription factor HUGO gene symbols and their respective Entrez Gene NCBI gene identifier. This gene list might be useful as features for the literature triage systems.</p> <ul> <li> <p>Name: <a href="https://zenodo.org/record/2562967/files/GREEKC_Hackathon_hs_tf_hgnc_ncbigeneid.zip?download=1">GREEKC_Hackathon_hs_tf_hgnc_ncbigeneid.zip</a></p> </li> <li> <p>Example: ADNP 23394</p> </li> <li> <p>Format: tsv-separated columns (HUGO gene symbol Id, Entrez Gene Id)</p> </li> </ul> </li> <li> <p>Sentences_methods: A list sentences that contain automatically labeled mentions of experimental method that can be used to characterize TF-TG interactions. This dataset might be useful as a more fine-grained additional training set as a considerable number of sentences to describe TF-TG interactions together with the experimental technique used to characterize them.</p> <ul> <li> <p>Name: <a href="https://zenodo.org/record/2562967/files/GREEKC_Hackathon_sentences_methods.zip?download=1">GREEKC_Hackathon_sentences_methods.zip</a></p> </li> <li> <p>Example: 23675312 A 7 We used chromatin immunoprecipitation (ChIP) experiments to show that Bck2 localizes to the promoters of M/G1-specific genes, in a manner dependent on functional ECB elements, as well as to the promoters of G1/S and G2/M genes.</p> </li> <li> <p>Format: tsv-separated columns (PubMed ID (PMID), text type (A=abstract, T= Title), sentence number, sentence text string)</p> </li> </ul> </li> </ul>
Hackathon - TF-TG literature triage Baseline Files
<p><strong>Files needed to use the baseline system.</strong></p> <p>Instructions to use the baseline system:</p> <ol> <li> <p>Install DeLFT as explained in <a href="https://github.com/kermitt2/delft">https://github.com/kermitt2/delft</a> (<em>If you are not using GPU, it is </em><em>recomended</em><em> to change the requirements.txt to “tensorflow==1.8.0” instead of “tensorflow_gpu==1.8.0”)</em></p> </li> <li> <p>Download the optimized embeddings (Model_FastText.zip) available in this repository and place it inside the main delft folder.</p> </li> <li> <p>Copy the following files available in this repository:</p> <ol> <li> <p>Greekc.zip: Extract the contents to ./delft/data/models/textClassification</p> </li> <li> <p>greekClassifier.py: Copy to ./delft</p> </li> <li> <p>reader.py: Substitute in ./delft/delft/textClassification/reader.py</p> </li> <li> <p>Embedding-registry.json: Substitute in ./delft/embedding-registri.json</p> </li> <li> <p>Fasttext_300_opt.zip: Extract the contents to ./delft/data/db</p> </li> </ol> </li> <li> <p>To make predictions, change the line 96 in greekClassifier to point to the text you want to predict. Each new line should be an abstract. Then run python3 greekClassifier.py classify > path_to_predicted</p> </li> </ol>
A Deep Learning Approach to Automated Bug Triaging: Investigating the Impact of Text Vectorization Methods on Effectiveness of CNN-LSTM
<p>The dataset used in the article 'A Deep Learning Approach to Automated Bug Triaging: Investigating the Impact of Text Vectorization Methods on Effectiveness of CNN-LSTM.'</p>
Engaging Informal Health Care Providers on Case Detection and Treatment Initiation Rates for TB and HIV in Rural Malawi (Triage Plus)
ClinicalTrials.gov study NCT02127983. IPD Sharing: Not stated. Countries: 1. Publications: 1.
PT-led Triage for Patients With Hip o Knee Osteoarthritis
ClinicalTrials.gov study NCT04665908. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Using Video for Triage of Children With Fever at the Medical Helpline 1813 in Copenhagen, Denmark
ClinicalTrials.gov study NCT04074239. IPD Sharing: NO. Countries: 1. Publications: 18.
Immediate Unselected Coronary Angiography Versus Delayed Triage in Survivors of Out-of-hospital Cardiac Arrest Without ST-segment Elevation
ClinicalTrials.gov study NCT02750462. IPD Sharing: NO. Countries: 1. Publications: 5.
The Effect of Telephone Symptom Triage Protocols in Patients With Cancer Therapy (TeleTRIAGE)
ClinicalTrials.gov study NCT04162717. IPD Sharing: NO. Countries: 1. Publications: 1.
Triage Strategies in Cervical Cancer Prevention
ClinicalTrials.gov study NCT02510027. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Tuberculosis Diagnostic Trial of CAD4TB Screening Alone Compared to CAD4TB Screening Combined With a CRP Triage Test, Both Followed by Confirmatory Xpert MTB/RIF Ultra in Communities of Lesotho and So
ClinicalTrials.gov study NCT05526885. IPD Sharing: YES. Countries: 2. Publications: 2.
Triage - Symptoms and Other Predictors in an All-comer Emergency Department Population; (EKBB 236/13)
ClinicalTrials.gov study NCT03892551. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Severity Indices of Diquat Poisoning for Triage and Prognosis in Acute Diquat Poisoning
ClinicalTrials.gov study NCT05215457. IPD Sharing: NO. Countries: 1. Publications: 13.
Can Prediction Models Triage Trauma Patients More Accurately Than Clinicians?
ClinicalTrials.gov study NCT02838459. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Work of Breathing Assessment in Triage Scale
ClinicalTrials.gov study NCT05479929. IPD Sharing: YES. Countries: 1. Publications: 8.
Lactate Use as Triage Tool in Sepsis : Veinous, Capillary or Arterial?
ClinicalTrials.gov study NCT01964690. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.