Find research datasets worth reusing

opencc-by-4.0Jun 2022View details →

Patient and GP experiences of implementing a medication review intervention in older people with multimorbidity: process evaluation of the SPPiRE trial

Abstract Background: The SPPiRE cluster randomised controlled trial (RCT) found that a GP delivered medication review that incorporated screening potentially inappropriate prescriptions (PIP), a brown bag review and a patient priority assessment, resulted in a significant but small reduction in the number of medicines and no significant reduction in PIP. Objective: To explore the experiences of GPs and patients engaged in the SPPiRE intervention and the potential for system wide implementation. Design: Mixed methods process evaluation; quantitative data was collected from the SPPiRE intervention website and qualitative data via semi-structured interviews. Setting and participants: 51 general practices throughout Ireland, and 404 participants with multimorbidity aged ≥65 years, prescribed ≥15 medicines participated in the RCT. Qualitative data was collected with purposive samples of intervention GPs (18/26) and patients (27/208).    Methods: Quantitative data was analysed descriptively, qualitative data thematically and both were integrated using a triangulation protocol. Results: The analysis generated three themes, intervention implementation, mechanisms of action, and both were underpinned by the theme of context. One fifth of patients had no review, primarily due to insufficient GP time. The brown bag review component resulted in the most deprescription of medications. GPs felt it easier to change medicines if the patient was well known to them, and patients were generally receptive to change. GPs identified lack of integration into practice software systems and resources as barriers to future implementation. Conclusion: Consideration of implementation of successful interventions is key to informing policy and integration into clinical practice. GPs and patients viewed the intervention positively, but implementation will depend on resourcing and integration into practice software systems. Trial registration number: <a href="https://doi.org/10.1186/ISRCTN12752680">ISRCTN12752680</a>

opencc-zeroJun 2024View details →

Dataset: GreenPower Motor Company Inc. (GP) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →

Dataset: GP-Act III Acquisition Corp. (GPATU) Stock Performance

opencc-zeroJun 2024View details →

Dataset: Plains GP Holdings, L.P. (PAGP) Stock Performance

opencc-by-4.0Dec 2015View details →

Text-fig. 3. Schema of radial section of G. rudolphii (sample 99/04). t – tracheid, r – ray, bp – bordered pit, tp – taxodioid cross-field pit, gp – glyptostroboid cross-field pit. in New Fossil Woods From The Paleogene Of Doupovské Hory And České Středohoří Mts. (Bohemian Massif, Czech Republic)

Text-fig. 3. Schema of radial section of G. rudolphii (sample 99/04). t – tracheid, r – ray, bp – bordered pit, tp – taxodioid cross-field pit, gp – glyptostroboid cross-field pit.

opencc-by-4.0Sep 2022View details →

Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)

This video is the sixth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022. Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading) Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Bing Wang is currently a PhD candidate in informatics and system science at the Informatics Research Center, Henley Business School, University of Reading. Bing’s research interests are Natural Language Processing, Machine Learning and Graph Machine Learning. Bing been working as a data scientist at Royal Berkshire NHS Foundation Trust since December 2019 during his PhD. Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/W6EH5l80NmU

opencc-by-4.0Sep 2022View details →

GP-net: Grasp Proposal for Mobile Manipulators

This dataset includes all trained models and data to use and replicate our work for GP-net (under review for ICRA 2023). The pre-trained model for using GP-net on a PAL TIAGo mobile manipulator with a parallel jaw gripper is available in GP-net_model_pretrained.zip. It can be used with our <a href="https://github.com/AuCoRoboticsMU/gpnet-ros">gpnet-ros</a> repository. If you want to use our dataset and train an alternative network architecture from scratch, use GP-net_training_data.zip. You can use it with the <a href="https://github.com/AuCoRoboticsMU/GP-net">GP-net</a> repository to train models. If you want to run simulation experiments with your trained model, download GP-net_simulation_data.zip for the URDFs of the test objects and pal gripper. If you want to generate a new dataset for an alternative gripper, you can use the <a href="https://github.com/AuCoRoboticsMU/gpnet-data">gpnet-data</a> repository, which is based on DexNet2.0. The docker image to run the code is available here under gpnet-data_docker_image.tar

opencc-byMar 2020View details →

Russian GP-5 Gas Mask - (Filter Damaged)

GP-5 gas mask is a Soviet-made single-filter gas mask. It was issued to the Soviet population starting in 1962; production ended in 1990. It is a lightweight mask, weighing 1.09 kg (Wikipedia) https://en.wikipedia.org/wiki/GP-5_gas_mask This model was made by hand without mesurements. scale and parts vary from actural GP-5 sizes and shapes. - **Free for use with credit given "G-P5 Gas Mask by MattOades AKA MattMakesSwords"** - **if you would like to use without credit this can be orginized, PM me.** - **Please let me know what you are, or are gonna use it for below I would love to know** - **if you do download, please consider following me on twitter https://twitter.com/MattMakesSwords** Thanks! *it goes without saying but this is not free for school assignements. if you are planning on becoming a plagerists and handing this in, have athink about why, IT IS MORE IMPORTANT TO LEARN! close the browser, call your lecturer and ask retake the class. :) goodluck, I believe in you* Source: Objaverse 1.0 / Sketchfab

opencc-by-4.0May 2021View details →

Study protocol and data dictionary: Effectiveness of a GP delivered medication review in reducing polypharmacy and potentially inappropriate prescribing in older patients with multimorbidity in Irish primary care: a cluster randomised controlled trial (SPPiRE study)

Methods Study design and participants The methods for the SPPiRE cluster RCT have been described in the trial protocol (21). This study is reported in line with the CONSORT 2010 cluster RCT checklist (22), see Appendix 1, and was approved by the Irish College of General Practitioners Research Ethics Committee. In brief, SPPiRE was a pragmatic two arm cluster RCT, with the intervention delivered to GP clusters and analysis of outcomes at the patient level. Information about the trial was publicised through a variety of GP research, teaching and training networks throughout Ireland. Eligible practices expressing an interest were formally invited. Practices were eligible to participate if they had at least 300 registered patients aged ≥65 years (based on the need to identify a sufficient number of eligible participants) and used either of the two Irish GP practice management systems (PMS) with over 80% national cover; this enabled use of a SPPiRE patient finder tool which was developed and embedded into these systems. Practices were excluded if they were currently involved in a medication management or prescribing trial or if they were unable to recruit at least five participants. Eligible patients were aged ≥65 years and prescribed ≥15 repeat medicines. A repeat medicine was defined as any unique item with a World Health Organisation Anatomical Therapeutic Chemical code on the patient’s current repeat prescription. Patients were excluded if they had been recruited into a practice that was unable to recruit at least four other participants, they were judged by their GP as unable to give informed consent or they were unable to attend the practice for a face to face medication review, (e.g. nursing home residents and house bound patients).  Recruited GPs ran the SPPiRE patient finder tool and screened the generated list to ensure only eligible patients were invited. Practices who identified more than 40 eligible patients were supported in selecting a random sample of 30 patients to invite. All recruited practices and patients gave fully informed consent and baseline data was collected prior to practice allocation, to reduce the likelihood of selection bias. Randomisation and masking Recruited practices were allocated to intervention or control groups by minimisation using Minimpy software (23) by the trial statistician (FB) who had no knowledge of participating practices. Minimisation variables included practice size (number of GP sessions per week, 0-14, 14-28 and 28 or more) and location (urban, rural or mixed). Considering the nature of the intervention, it was not possible to blind GPs or patients to the intervention, however to reduce the risk of detection bias the two primary outcome measures; the number of repeat medicines and whether a PIP was present were assessed by an independent blinded pharmacist (MF). Procedures Intervention GPs received unique login details to the SPPiRE website where they had access to five training videos and a template for performing the SPPiRE medication review. The training videos provided background information on multimorbidity and polypharmacy, PIP, eliciting patient treatment priorities and conducting a brown bag medication review. GPs were instructed to book a double appointment and to ask their patients to bring all their medicines in to the medication review visit with them. The SPPiRE medication review process had two main components; gather and record information and then to discuss and agree changes with their patient based on the recorded information, with a focus on deprescribing medicines that were potentially inappropriate, figure 1. The website provided suggested treatment alternatives for identified PIP but all treatment decisions were ultimately at the discretion of the individual GP, based on their clinical judgement and their patients’ individual priorities. Control GPs delivered usual care during the six to twelve month study period. At the time of intervention delivery there was no structured chronic disease management programme in Irish primary care and many patients with multimorbidity attended multiple hospital specialists. In Ireland, the majority of people aged ≥70 years of age have access to free GP visits and medicines with some prescription charge co-payments. In the 65 – 69 year old age category a lower proportion have access to both free GP visits and prescription medicines. Access to specialists and diagnostics in secondary care is free for the entire population. Outcomes The two primary outcomes were the number of repeat medicines and the proportion of patients with any PIP, from a list of 34 pre-specified indicators (see Appendix 2). A series of secondary prescribing related outcome measure were pre-specified to allow a more in depth analysis of the effect of the intervention on prescribing. These were: <ul> <li>The number of medicines stopped and started</li> <li>The proportion of patients with a reduction in significant polypharmacy (defined as ≥15 repeat medicines)</li> <li>The number of PIP</li> <li>The proportion of patients with a high risk PIP (see Appendix 2)</li> <li>The proportion of patients with any reduction in PIP</li> </ul> Secondary patient reported outcomes measures were included to capture the effectiveness of the intervention from the patients’ perspective. These were: <ul> <li>Health related Quality of life (EQ5D-5L)(24)</li> <li>Revised Patients' attitudes towards deprescribing (rPATD)  (25)</li> <li>Multimorbidity Treatment Burden Questionnaire (MTBQ) (26)</li> </ul> Health care utilisation data was collected to assess the effect of the intervention on health care usage and for the trial’s economic evaluation. Outcomes were collected at baseline and at six months after intervention delivery. Patient reported measures were collected by postal questionnaires. Data for all other measures including prescribed medicines, medical and investigations history and healthcare utilisation were collected by participating GPs and submitted to the study manager (CMC). This was a deviation from the original protocol, which indicated this data would be collected by the research team. This deviation related to changes in data protection and national health research regulations during the study period, which precluded research team access to the patients’ full clinical record. Adverse events Information on adverse events such as mortality, ED presentations and hospital admissions was collected at follow up. Given the deprescribing approach of the intervention a safety protocol for identifying and reporting any suspected adverse drug withdrawal events (ADWEs) was developed. An ADWE is defined as either recurrence of the condition for which the drug was prescribed (e.g. recurrence of angina after stopping a beta blocker) or a physiologic reaction to drug withdrawal (e.g. SSRI withdrawal syndrome)  (27, 28). Although discontinuing medicines in older people has been demonstrated to be safe (29), given the paramount importance of the principle of “do no harm” in research ethics a vigorous and detailed method was established to ensure that any potential ADWEs precipitated by deprescribing in a SPPiRE medication review were captured. Intervention GPs were asked to report any possible ADWE following the SPPiRE medication review. The Naranjo ADR probability scale (30) has been adapted in other studies to assess the likelihood a reaction is related to drug withdrawal  (27, 28). This tool was further adapted for SPPiRE and used to make an assessment on the causality of the ADWE. To ensure the patient perspective was included, self-reported possible ADWEs were also collected from patient follow up questionnaires.     Sample size As outlined in the trial protocol (21), the study was designed with 90% power to detect a 20% reduction in the proportion with PIP and a mean difference of one medicine between intervention and control groups (based on a mean of 17.4 medicines SD (2.6)) and the sample size inflated to incorporate the effects of clustering (using an ICC of 0.025). The sample size was recalculated when it became apparent during early recruitment that it would not be possible to recruit clusters with an average of 15 participants, as was initially planned in the protocol. An average cluster size of eight was anticipated which inflated the original sample size from 30 practices (450 patients) to 50 practices (400 patients). Statistical analysis Descriptive statistics were used to describe baseline characteristics of recruited practices and participants. All analyses were conducted under the intention-to-treat principle and those lost to follow up had their baseline data carried forward. The primary analysis was carried out using multi-level modelling. The first primary outcome measure, number of repeat medications, was assessed using mixed effects Poisson regression with the individual as the unit of analysis and the practice included as the random effect to control for the effects of clustering and results presented using incidence rate ratios (IRR) and 95% confidence intervals (CI). The baseline number of medicines, GP size (number of GP sessions per week) and GP location (urban/rural) were included in the analysis as fixed effects. The second outcome measure, proportion of patients with a PIP, was analysed in a similar manner using mixed effects logistic regression, including PIP at baseline, GP size and location, and results presented using odd ratios (OR) and 95% CIs. A number of pre-specified sensitivity analyses were conducted; complete case analysis, per protocol analysis and including “presence of a repeat prescribing policy” as a covariate. All secondary outcomes were analysed in a similar manner to the primary outcomes, using appropriate mixed effects regression methods (i.e. linear, logistic, Poisson).   Note: Version 3 (published 28 April 2025) updates Version 2 by removing Participant GP1P4 following consent withdrawal. This version should be used for all future analyses.

opencc-by-4.0Mar 2024View details →

Pulsars detected in the GP survey in only Stokes I images

The known pulsars detected in the GP survey in only Stokes I images. The mean flux density of the pulsars from the Stokes I image from this work is denoted by SI and the flux density from the Stokes V image is denoted by SV. α refers to the spectral index of the pulsars. Only two of the pulsars, PSRs J1614- 5048 and J1644-4559 have a previously recorded flux density below 300 MHz by Frail et al. (2016). For the 14 remaining pulsars, this is the first low-frequency detections below 300 MHz.

opencc-by-4.0Mar 2024View details →

The known pulsars detected in the GP survey in only beamformed searches.

The known pulsars detected in the GP survey in only beamformed searches. It shows the names and parameters of the pulsars. Detailed analysis of these pulsars can be found in Xue et al. (2017) and Bhat et al. (2023b). As this work is mainly focused on the imaging aspect of pulsar searching, these pulsars are not included as part of the analysis done for this work.

Safety and Efficacy of Turoctocog Alfa Pegol (N8-GP) in Previously Untreated Patients With Haemophilia A

ClinicalTrials.gov study NCT02137850. IPD Sharing: Not stated. Countries: 23. Publications: 2.

Safety, Tolerability and Immunogenicity Study of Different Vaccine Regimens of Trivalent Ad26.Mos.HIV or Tetravalent Ad26.Mos4.HIV Along With Clade C Glycoprotein (gp)140 in Healthy Human Immunodefici

ClinicalTrials.gov study NCT02788045. IPD Sharing: Not stated. Countries: 2. Publications: 1.

closedIPD-NOFeb 2026View details →

The Effect of Concord Grape Polyphenol-soy Protein Isolate Complex (GP-SPI) on Gut Microbiota

ClinicalTrials.gov study NCT04018066. IPD Sharing: NO. Countries: 1. Publications: 1.

Effect of Tepotinib on the PK of the P-gp Substrate Dabigatran Etexilate

ClinicalTrials.gov study NCT03492437. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Immunogenicity of Recombinant Vesicular Stomatitis Vaccine for Ebola-Zaire (rVSV[Delta]G-ZEBOV-GP) for Pre-Exposure Prophylaxis (PREP) in People at Potential Occupational Risk for Ebola Virus Exposure

ClinicalTrials.gov study NCT02788227. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.

Safety and Efficacy of Nonacog Beta Pegol (N9-GP) in Previously Untreated Patients With Haemophilia B

ClinicalTrials.gov study NCT02141074. IPD Sharing: Not stated. Countries: 24. Publications: 1.