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330 results for “medical care”

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zenodo44/100

Data relating to Chiedozie et al. How many medications do doctors in primary care use? An observational study of the DU90% indicator in primary care in England.

<p>Data in .csv format relating to the paper Chiedozie et al. 2020 &quot;How many medications do doctors in primary care use? An observational study of the DU90% indicator in primary care in England.&quot; Also contains eTables 5-8&nbsp;in Excel format, and Stata do file for deriving the DU90% indicator.</p>

opencc-by-4.0Jun 2020View details →
dryad36/100

Data from: Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in primary care: an interrupted time series analysis

<p><strong>Background</strong>. We evaluated the impact of a pharmacist-led Safety Medication dASHboard (SMASH) intervention on medication safety in primary care.<br> <strong>Methods and findings</strong>. SMASH comprised: (1) training of clinical pharmacists to deliver the intervention; (2) a web-based dashboard providing actionable, patient-level feedback; and (3) pharmacists reviewing individual at-risk patients, and initiating remedial actions or advising general practitioners on doing so. It was implemented in forty-three general practices covering a population of 235,595 people in Salford (Greater Manchester), UK. All practices started receiving the intervention between 18 April 2016 and 26 September 2017. We used an interrupted time series analysis of rates of potentially hazardous prescribing and inadequate blood-test monitoring, comparing observed rates post-intervention to extrapolations from a 24-month pre-intervention trend. The number of people registered to participating practices and having one or more risk factors for being exposed to hazardous prescribing or inadequate blood-test monitoring at the start of the intervention was 47,413 (males: 23,073 [48.7%]; mean age: 60 [standard deviation: 21]). At baseline, 95% of practices had rates of potentially hazardous prescribing (composite of 10 indicators) between 0.88% and 6.19%. The prevalence of potentially hazardous prescribing reduced by 27.9% (95% confidence interval [CI], 20.3% to 36.8%) at 24 weeks and by 40.7% (95% CI, 29.1% to 54.2%) at twelve months after introduction of SMASH. The rate of inadequate blood-test monitoring (composite of 2 indicators) reduced by 22.0% (95% CI, 0.2% to 50.7%) at 24 weeks and by 23.5% (95% CI, -4.5% to 61.6%) at 12 months. After 12 months, 95% of practices had rates of potentially hazardous prescribing between 0.74% and 3.02%. We did not randomise practices but enrolled them in a naturalistic fashion. All our measurements were based on routinely kept electronic health records.<br> <strong>Conclusions</strong>. The SMASH intervention was associated with reduced rates of potentially hazardous prescribing and inadequate blood-test monitoring in general practices. This reduction was sustained over 12 months after start of the intervention for prescribing but not for monitoring of medication. There was a marked reduction in the variation in rates of high-risk prescribing between practices.</p>

opencc-zeroAug 2020View details →
zenodo36/100

Individualized Nutritional Support versus Usual Care in Medical Inpatients at Risk of Malnutrition: Randomized Trial

<p>Dataset for analysis of the trial &quot;Individualized Nutritional Support versus Usual Care in Medical Inpatients at Risk of Malnutrition&quot;</p>

opencc-by-4.0Oct 2020View details →
zenodo36/100

Microblogging violent attacks on medical staff: A case study of the Longmen County People’s Hospital incident, an indication of a health care crisis in China

<p>A violent attack on medical staff in Guangdong Province, in which a female doctor at Longmen County People&rsquo;s Hospital (LCPH) was severely injured by a knife-wielding patient, has drawn significant public attention to the phenomenon of hospital violence and initiated discussions on how to resolve violence in hospitals. Social networking sites, such as Sina Weibo, a Chinese version of Twitter, have played a role in this public debate. The incident at LCPH provides an opportunity to examine how Weibo has been used in the debate about violence against medical staff in China.&nbsp;Using the Sina Weibo&rsquo;s built-in search tool, we established a dataset of 661 Chinese-language micro-blogs containing the search terms: Longmen (&ldquo;龙门&rdquo;), doctor (&ldquo;医生&rdquo;), and slash (&ldquo;砍&rdquo;) that were posted between July 15, 2015, the date of the violent incident at LCPH, and August 15, 2015. We performed a content analysis of the micro-blogs to examine: users&rsquo; demographics, attitudes toward the injured doctor and the attacker, possible reasons for the hospital violence, and proposed measures for preventing doctors from violent incidents.&nbsp;</p>

opencc-zeroJun 2016View details →
zenodo36/100

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)

<p><strong>Methods</strong></p> <p><strong>Study design and participants</strong></p> <p>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 &ge;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.</p> <p>Eligible patients were aged &ge;65 years and prescribed &ge;15 repeat medicines. A repeat medicine was defined as any unique item with a World Health Organisation Anatomical Therapeutic Chemical code on the patient&rsquo;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).&nbsp; 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.</p> <p><strong>Randomisation and masking</strong></p> <p>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).</p> <p><strong>Procedures</strong></p> <p>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&rsquo; individual priorities.</p> <p>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 &ge;70 years of age have access to free GP visits and medicines with some prescription charge co-payments. In the 65 &ndash; 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.</p> <p><strong>Outcomes</strong></p> <p>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:</p> <ul> <li>The number of medicines stopped and started</li> <li>The proportion of patients with a reduction in significant polypharmacy (defined as &ge;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> <p>Secondary patient reported outcomes measures were included to capture the effectiveness of the intervention from the patients&rsquo; perspective. These were:</p> <ul> <li>Health related Quality of life (EQ5D-5L)(24)</li> <li>Revised Patients' attitudes towards deprescribing (rPATD)&nbsp;&nbsp;(25)</li> <li>Multimorbidity Treatment Burden Questionnaire (MTBQ)&nbsp;(26)</li> </ul> <p>Health care utilisation data was collected to assess the effect of the intervention on health care usage and for the trial&rsquo;s economic evaluation.</p> <p>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&rsquo; full clinical record.</p> <p><strong>Adverse events</strong></p> <p>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)&nbsp;&nbsp;(27, 28). Although discontinuing medicines in older people has been demonstrated to be safe (29), given the paramount importance of the principle of &ldquo;do no harm&rdquo; 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&nbsp;&nbsp;(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.&nbsp;&nbsp;</p> <p>&nbsp;</p> <p><strong>Sample size</strong></p> <p>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).</p> <p><strong>Statistical analysis</strong></p> <p>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 &ldquo;presence of a repeat prescribing policy&rdquo; 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).</p> <p>&nbsp;</p> <p>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.</p> <p>&nbsp;</p>

opencc-by-4.0May 2021View details →
zenodo36/100

Evaluation of comparative Efficacy of two routes of administration of Carathmus Tinctorius (Medicated Enema and Nasal Administration) as an Adjuvant Therapy with Standard Care With Physiotherapy in the Rehabilitation of Thrombolytic Stroke (Pakshaghat): A Randomized Controlled Trial Protocol

Open the record for dataset details and reuse information.

opencc-by-4.0Apr 2024View details →
zenodo36/100

Generative AI in Healthcare: Revolutionizing Patient Care and Medical Innovation

<p><a href="https://autorexa.com/transforming-patient-care-the-role-of-generative-ai-in-modern-healthcare/">Generative AI in Healthcare</a> is revolutionizing the medical field by enhancing diagnostics, accelerating drug discovery, enabling personalized treatments, and improving patient engagement. From creating synthetic medical images for training to developing tailored treatment plans, this technology is transforming patient care and medical research. With its ability to analyze vast datasets and automate complex processes, generative AI is driving efficiency, reducing costs, and delivering precise solutions. Learn how generative AI is shaping the future of healthcare with innovation, accuracy, and accessibility.</p>

opencc-by-4.0Dec 2024View details →
zenodo36/100

A concept for FAIR clinical medication data usage - From care to research with OMOP: literature list of OHDSI studies

<p>This list of papers has been reviewed for the usage of drug data and to answer the question on what drug level the study was done.&nbsp;</p> <p>We checked whether drug ingredient level or drug component with dose and unit was required for the studies.&nbsp;</p>

opencc-by-4.0Jul 2021View details →
zenodo36/100

COMPASS: A magnetic particle-based method for rapid and highly sensitive medical point-of-care diagnostic

<p>This Git consists of all data sets used for generating the figures for the manuscript:</p> <p>COMPASS: A magnetic particle-based method for&nbsp;rapid and highly sensitive medical point-of-care diagnostic</p> <p>Specific information can be find in the readme</p>

opencc-by-4.0Sep 2022View details →
ClinicalTrials.gov36/100

Effect of Peer Support Intervention on Medication Adherence, Self-care and Knowledge Among Patients With Diabetes

ClinicalTrials.gov study NCT07145983. IPD Sharing: NO. Countries: 1. Publications: 16.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov36/100

Group Medical Appointments for Intensive Lifestyle Treatment for Obesity in Cleveland Clinic Primary Care Practices

ClinicalTrials.gov study NCT07268417. IPD Sharing: NO. Countries: 1. Publications: 4.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov36/100

Bringing Care to Patients: Patient-Centered Medical Home for Kidney Disease

ClinicalTrials.gov study NCT02270515. IPD Sharing: Not stated. Countries: 1. Publications: 4.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

Comparison of MedBook Portal and Usual Care in Medication Reconciliation at Primary Healthcare Upon Hospital Discharge

ClinicalTrials.gov study NCT06517160. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov36/100

Motivational Interview Intervention to Help Patients Formulate Their Goals for Medical Care in the Emergency Department

ClinicalTrials.gov study NCT03208530. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

24 Hour Intensivist Coverage in the Medical Intensive Care Unit

ClinicalTrials.gov study NCT01434823. IPD Sharing: Not stated. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

PTSD After ICU Survival - Caring for Patients With Traumatic Stress Sequelae Following Intensive Medical Care

ClinicalTrials.gov study NCT03315390. IPD Sharing: YES. Countries: 1. Publications: 3.

controlledIPD-YESFeb 2026View details →
ClinicalTrials.gov36/100

Pilot Study to Assess Palonosetron Versus Ondansetron as Rescue Medication in Subjects That Develop Postoperative Nausea and Vomiting (PONV) in the Postanesthesia Care Unit (PACU)

ClinicalTrials.gov study NCT00967499. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

Measuring Quality of Medical Student Performance at Contextualizing Care

ClinicalTrials.gov study NCT01088438. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

Adjunct Methadone to Decrease the Duration of Mechanical Ventilation in the Medical Intensive Care Unit

ClinicalTrials.gov study NCT02025855. IPD Sharing: Not stated. Countries: 1. Publications: 8.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

Conversational AI in Tactical Casualty Care: Baseline GPT-4o Improves Combat Medic Decision-Making

ClinicalTrials.gov study NCT06796036. IPD Sharing: NO. Countries: 1. Publications: 2.

closedIPD-NOFeb 2026View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record