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4,954 results for “hospitals”
Dortmund St. Johannes-Hospitals Apotheke
<p>Historical questionnaire/s 1924/1948 and index cards, partly selected enclosures regarding the history of a <br>German pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948 und Karteikarten, ggf. <br>gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope): <br>https://kalliope-verbund.info/DE-611-BF-70963<br>[Funktion: Im Findbuch anzeigen]<br>Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannot <br>be published due to copyright or other legal restrictions (NB: Das Katalogisat bei Kalliope kann auch auf <br>Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oder urheberrechtlichen Gründen nicht <br>veröffentlicht werden dürfen).</p>
Case report for Assessment and impact in quality-of-life post radiotherapy in breast cancer patients treated at Acharya Vinoba Bhave Rural Hospital (AVBRH), Sawangi, 2023 - 2024
<p>Case report for Assessment and impact in quality-of-life post radiotherapy in breast cancer patients treated at Acharya Vinoba Bhave Rural Hospital (AVBRH), Sawangi, 2023 - 2024</p>
QOL questionnaires for Assessment and impact in quality-of-life post radiotherapy in breast cancer patients treated at Acharya Vinoba Bhave Rural Hospital (AVBRH), Sawangi, 2023 - 2024
<p>QOL questionnaires for Assessment and impact in quality-of-life post radiotherapy in breast cancer patients treated at Acharya Vinoba Bhave Rural Hospital (AVBRH), Sawangi, 2023 - 2024</p>
Prevalence, resistance profiles and factors associated with skin and soft-tissue infections at Jinja regional referral hospital: A retrospective study
<p>Skin and soft-tissue infections (SSTI) are common cases of hospital-acquired infections with aetiologic agents exhibiting antimicrobial resistance (AMR). We determined the prevalence, proportion of laboratory-investigated cases, AMR-profiles, and factors associated with SSTI and multi-drug resistance (MDR). This study was based on archived data of patients suspected of SSTI from 2019-2021 at Jinja Regional Referral Hospital. The analysis involved 268 randomly selected patient reports. Prevalence of SSTI was 66.4%. Laboratory-investigated cases were 14.11%. <em>Staphylococcus aureus </em>(n=51) was the most isolated organism. MDR pathogens explained 47% of infections. Methicillin-resistant <em>S. aureus</em> was up to 44%. In addition, 61% of Gram-negatives had the potential to produce extended-spectrum beta-lactamases, while 27% were non-susceptible to carbapenems. Ward of admission was significantly associated with infection (aPR=1.78, 95% CI: 1.003-3.18, p-value=0.04). Age category <u>(</u>19-35) was an independent predictor for MDR infections (aPR=2.30, 95%CI:1.02-5.23, p-value=0.04). The prevalence is relatively high with MDR pathogens responsible for almost half of the infections. Routine use of culture and sensitivity testing should be done for proper infection management. Gentamicin and ciprofloxacin can be considered for empirical management of emergency SSTI suspected of <em>S. aureus</em>. Recognizing SSTI under the Global Antimicrobial resistance Surveillance System (GLASS) would lead to improved preparedness and response to AMR.</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>
Mechanisms of early glucose regulation disturbance after out-of-hospital cardiopulmonary resuscitation: an explorative prospective study
<p>Laboratory results of "Mechanisms of early glucose regulation disturbance after out-of-hospital cardiopulmonary resuscitation: an explorative prospective study".</p>
Financial forensics for Greek Public Hospitals
<p>Balance sheet analysis- financial statements data of Greek Public Hospitals (2015-2021), examined using the Benford Law and Beneish model tools.</p>
Supplementary Materials for the Article: Sex-Related Differences in the Association of Obesity with Outcomes in Out-of-Hospital Cardiac Arrest Patients
<p>Supplementary Materials - Sex-Related Differences in the Association of Obesity Described by Emergency Medical Teams on Outcomes in Out-of-Hospital Cardiac Arrest Patients<br><br></p> <p><strong>Supplementary Table 1</strong><br><em>Opis</em>: Detailed report on models related to location and initial rhythm, including odds ratios and confidence intervals for cardiac arrest outcomes.</p> <p><strong>Supplementary Table 2</strong><br><em>Opis</em>: Further analysis of initial rhythm and location, adjusted by sex, age, and other factors affecting cardiac arrest outcomes.</p> <p><strong>Supplementary Table 3</strong><br><em>Opis</em>: Assessment of multicollinearity between variables used in logistic regression models, based on the Variance Inflation Factor (VIF).</p> <p><strong>Supplementary Table 4</strong><br><em>Opis</em>: Results of a three-way logistic regression model showing the combined effects of sex, location, and initial rhythm on the odds of Return of Spontaneous Circulation (ROSC).</p> <p><strong>Supplementary Table 5</strong><br><em>Opis</em>: A logistic regression model demonstrating how location and age modulate ROSC odds, with and without interaction with initial rhythm.</p>
Assessment of Patient Safety Culture and Associated Factors among Healthcare Professionals in Public Hospitals of Bahir Dar City, Northwest Ethiopia: A Mixed-Methods Study
<p><span>This dataset contains raw data of "Socio-Demographic and Socio-Economic, and organizational and work related characteristics" on the assessment of patient safety culture among healthcare professionals in public hospitals of Bahir Dar City, Northwest Ethiopia.</span></p>
Dataset Bibliometric - Leadership Style In Tourism and Hospitality
<p>Dataset Bibliometric - Leadership Style In Tourism and Hospitality In 2010 -2024</p>
Search strategies for a systematic review of tools used for costing outbreaks in acute hospital settings
<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: </p> <p><span>What costing tools have been developed for use by healthcare workers to measure resource utilisation (and or cost data) attributable to outbreaks in acute hospitals from the healthcare perspective? </span></p>
Data from: Identifying patterns of non-communicable diseases in developed eastern coastal China: a longitudinal study of electronic health records from 12 public hospitals
Objective: Few studies have examined the spectrum and trends of non-communicable diseases (NCDs) in inpatients in eastern coastal China, which is transforming from an industrial economy to a service-oriented economy and is the most economically developed region in the country. This study aimed to dynamically elucidate the spectrum and characteristics of severe NCDs in eastern coastal China by analysing patients' longitudinal electronic health records (EHRs). Setting: To monitor the spectrum of NCDs dynamically, we extracted the EHR data from 12 general tertiary hospitals in eastern coastal China from 2003 to 2014. The rankings of and trends in the proportions of different NCDs presented by inpatients in different gender and age groups were calculated and analysed. Participants: We obtained a total sample of 1,907,484 inpatients with NCDs from 2003 to 2014, 50.05% of whom were male and 81.53% were aged 50 years or older. Results: There was an increase in the number of total NCD inpatients in eastern coastal China from 2003 to 2014. However, the proportion of chronic respiratory diseases and cancer inpatients decreased over the 12-year period. Compared with men, women displayed a significant increase in the proportion of mental and behavioural disorders (P<0.001) over time. Additionally, digestive diseases and sensory organ diseases significantly decreased among men, but not women. The older group accounted for a larger and growing proportion of the NCD inpatients, and the most common conditions in this group were cerebral infarctions, coronary heart disease and hypertension. In addition, the proportion of 21- to 50-year-old inpatients with diabetes, blood diseases or endocrine diseases skyrocketed from 2003 to 2014 (P<0.001). Conclusions: The burden of inpatients' NCDs increased rapidly, particularly among women and younger people. The NCD spectrum observed in eastern coastal China is a good source of evidence for developing prevention guides for regions experiencing transition.
Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database
<p><b>Objective:</b> The predictors of in-hospital mortality for intensive care units (ICU)-admitted HF patients remain poorly characterized.We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients.</p> <p><b>Design: </b>A retrospective cohort study.</p> <p><b>Setting and Participants: </b>Data were extracted from the MIMIC-III database. Data on 1,177 heart failure patients were analysed.</p> <p><strong>Methods</strong>: Patients meeting the inclusion criteria were identified from the MIMIC-III database and randomly divided into derivation and validation groups. Independent risk factors for in-hospital mortality were screened using XGBoost and LASSO regression models in the derivation sample. Multivariable logistic regression analysis was used to build prediction models. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. After pairwise comparison, the best performing model was chosen to build a nomogram according to the regression coefficients.</p> <p><b>Results:</b> Among the 1,177 admissions, in-hospital mortality was 13.52%. In both groups, the XGBoost, LASSO regression, and GWTG-HF risk score models showed acceptable discrimination. The XGBoost and LASSO regression models also showed good calibration. In pairwise comparison, the prediction effectiveness was higher with the XGBoost and LASSO regression models than with the GWTG-HF risk score model (P<0.05). The XGBoost model was chosen as our final model for its more concise and wider net benefit threshold probability range and was presented as the nomogram.</p> <p><b>Conclusions</b><b>:</b> Our nomogram enabled good prediction of in-hospital mortality in ICU-admitted HF patients, which may help clinical decision-making for such patients.</p>
Alert burden in pediatric hospitals: A cross-sectional analysis of six academic pediatric health systems using novel metrics
<p class="Pediatrics">Background: Excessive electronic health record (EHR) alerts reduce the salience of actionable alerts. Little is known about the frequency of interruptive alerts across health systems and how the choice of metric affects which users appear to have the highest alert burden.</p> <p class="Pediatrics">Objective: (1) Analyze alert burden by alert type, care setting, provider type, and individual provider across 6 pediatric health systems. (2) Compare alert burden using different metrics.</p> <p class="Pediatrics">Materials and Methods: We analyzed interruptive alert firings logged in EHR databases at 6 pediatric health systems from 2016-2019 using 4 metrics: (1) alerts per patient encounter, (2) alerts per patient-day, (3) alerts per 100 orders, and (4) alerts per unique clinician days (calendar days with at least one EHR log in the system). We assessed intra- and inter-institutional variation and how alert burden rankings differed based on the chosen metric.</p> <p class="Pediatrics">Results: Alert burden varied widely across institutions, ranging from 0.06 to 0.76 firings per encounter, 0.22 to 1.06 firings per inpatient-day, 0.98 to 17.42 per 100 orders, and 0.08 to 3.34 firings per clinician day logged in the EHR. Custom alerts accounted for the greatest burden at all 6 sites. The rank order of institutions by alert burden was similar regardless of which alert burden metric was chosen. Within institutions, the alert burden metric choice substantially affected which provider types and care settings appeared to experience the highest alert burden.</p> <p>Conclusion: Estimates of the clinical areas with highest alert burden varied substantially by institution and based on the metric used.</p>
Umbilical artery lactate levels and associated maternal and newborn characteristics at Mulago National Referral Hospital: A cross-sectional observational study
<p><b>Objective: </b>To determine the maternal and newborn characteristics associated with high umbilical artery lactate levels at Mulago National Referral Hospital.</p> <p><b>Design</b>: Cross-sectional observational study.</p> <p><b>Setting</b>: Department of Obstetrics and Gynecology at a national referral hospital located in the capital of Uganda, Kampala.</p> <p><b>Participants</b>: We randomly selected 720 pregnant mothers at term who presented in labour and their newborn babies.</p> <p><b>Primary outcome</b>: Umbilical artery lactate level.</p> <p><b>Results</b>: During the study, there were 579 vaginal deliveries (18 instrumental) and 141 caesarean sections which met the inclusion criteria. One hundred eighty seven neonates (187) had high arterial lactate levels. The following factors were associated with an increased likelihood of high lactate concentration<b>: </b>male<b> </b>sex (aOR = 1.71; 95%CI 1.16–2.54; <i>P<</i>0.05), primigravidity (aOR = 2.78; 95%CI 1.89-4.08; <i>P </i>< 0.001), Meconium stained liquor (MSL) (aOR = 5.85; 95%CI 4.08-8.47; P<0.001) and, Administration of oxytocin (aOR = 1.97; 95%CI 1.00-3.77; <i>P<</i>0.05).</p> <p><b>Conclusion</b>: About a fifth of the babies born in Mulago national referral hospital during the study period had high umbilical artery lactate. The maternal-fetal factors significantly associated with high umbilical artery lactate levels included: baby's sex, mother's gravidity, meconium stained amniotic fluid, and oxytocin administration during labour.</p>
Assessment of insight in hospitalized neurological patient: Cognitive profile and mood disorder
<p>Purpose:Many studies have evaluated the characteristics of insight, especially in psychiatric patient pop-ulations. However, this construct has been poorly examined within neurological disorders. We exploredthe relationship between altered insight, mood disorders and neurocognitive functioning in a sample ofpatients admitted to a neurological rehabilitation unit.Method:Each patient, at the time of hospitalization (T0), underwent an evaluation of the overall cogni-tive profile, of the frontal functions, and the compilation of scales aimed at investigating the 4 domainsunder examination of insight and anxiety-depressive symptomatology. After 3 months (T1), at the end ofthe rehabilitative and supportive treatment, the patients underwent the same evaluation performed atT0.Results:Our results showed significant differences between T0 and T1 in the variables examined relatedto insight. In particular, there was a correlation between the global cognitive profile, including executivefunctions, and all insight domains. This confirms how the degree of cognitive deficit, especially of exec-utive type, affects all levels of awareness of the individual.We have also found correlations between mood disorders and insight. In particular, our results showthat depression versus anxiety plays a fundamental role in a person’s awareness.Conclusions:The study of insight is fundamental not only for the relapses it could have on the patient, butalso on those to health care professionals. In fact, having an adequate insight could lead to a greater moti-vation of the patient to be more complimentary to pharmacological and rehabilitative therapies, alsofavoring social reintegration.</p>
An interactive tool to forecast us hospital needs in the Coronavirus 2019 pandemic
<p>We developed an application (https://rush-covid19.herokuapp.com/) to aid US hospitals in planning their response to the ongoing COVID-19 pandemic. Our application forecasts hospital visits, admits, discharges, and needs for hospital beds, ventilators, and personal protective equipment by coupling COVID-19 predictions to models of time lags, patient carry-over, and length-of-stay. Users can choose from seven COVID-19 models, customize a large set of parameters, examine trends in testing and hospitalization, and download forecast data.</p> <p>The data and scripts contained herein are used to generate Figure 1 of the associated manuscript, which presents general forms of the models used by our application and presents results for each model across time.</p>
Additional File 1 and Notebook Code for "Machine learning approaches for hospital acquired pressure injuries: a retrospective study of electronic medical records"
<p>Supplementary materials (Pressure_Injuries_Additional_File_1_final_double_blind.pdf) and Jupyter Notebook code (HAPI_Prediction_Script.pdf) developed as supplement for study "Machine learning approaches for hospital acquired pressure injuries: a retrospective study of electronic medical records".</p>
Data set for Predicting hospital occupancy for covid-19 patients: a simulation approach based on archetypes of empirical services' trajectories
<p>Data set for the paper: Predicting hospital occupancy for covid-19 patients: a simulation approach based on archetypes of empirical services’ trajectories</p> <p>Based on: Marin-Garcia, J. A., Ruiz, A., Julien, M., & Garcia-Sabater, J. P. (2021). A data generator for covid-19 patients’ care requirements inside hospitals. WPOM-Working Papers on Operations Management, 12(1), 76-115. https://doi.org/10.4995/wpom.15332</p> <p> </p>
Effects of Alcohol Consumption on Oxidative Stress in a Sample of Patients Recruited in a Dietary Center in a Southern University Hospital: A Retrospective Study
<p><em>Background and objectives</em>: The aim of this retrospective study was to evaluate the effects of alcohol consumption on oxidative stress. <em>Materials and Methods</em>: The study was conducted by analyzing the increase in lipid peroxidation, the reduction of antioxidant defenses and the alteration of the oxidation/antioxidant balance after the administration of ethanol in 25% aqueous solution (<em>v</em>/<em>v</em>) at a concentration of 0.76 g/kg of body weight daily in two doses for 3 days. The changes in oxidative stress indices were investigated by standard methods previously described. <em>Results</em>: Ethanol administration has determined a significant increase in plasma levels of lipid hydroperoxide (LOOH), malonilaldehyde (MDA) and oxidized glutathione (GSSH), and a decrease in total antioxidant capacity (TAC), reduced glutathione (GSH) and GSH/GSSH ratio. <em>Conclusions</em>: In the proposed experimental condition, the excessive and repeated consumption of ethanol causes oxidative damage, as shown by the increase in lipid peroxidation, the reduction of antioxidant defenses and the alteration of the oxidation/antioxidant balance, which, at least in part, are responsible for the harmful effects of excess ethanol.</p>
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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.
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.