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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 on "regression analyses" on the assessment of patient safety culture among healthcare professionals in public hospitals of Bahir Dar City, Northwest Ethiopia."</span></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 " </span><span>Likert Scale Descriptive Analysis"on t</span><span>he assessment of patient safety culture among healthcare professionals in public hospitals of Bahir Dar City, Northwest Ethiopia.</span></p>
A AVENTURA DE LUCCA NO HOSPITAL FUNDO DO MAR
<p> </p> <p> </p> <p>O produto técnico desenvolvido, intitulado "A Aventura de Lucca no Hospital Fundo do Mar", é uma animação com duração de 6 minutos e 50 segundos, direcionada a crianças em idade pré-letramento. A estrutura da animação inclui perguntas simples sobre os elementos visuais apresentados, permitindo um intervalo para que a criança raciocine e responda, seguido por uma narração amigável e soluções oferecidas pelos objetos animados. Este formato visa familiarizar as crianças com o ambiente hospitalar de maneira lúdica e acessível.</p> <p>O principal objetivo da animação é adaptar o contexto hospitalar ao universo infantil, proporcionando distração e promovendo a compreensão do ambiente hospitalar. Através de cenários e atividades lúdicas, a animação aborda aspectos da rotina hospitalar, reduzindo a ansiedade e o medo frequentemente associados à hospitalização, resultantes do desconhecimento dos procedimentos e cuidados médicos. Especificamente, a animação inclui atividades relacionadas aos medicamentos na enfermaria, às imagens de raio-X no setor de radiologia e ao preparo para cirurgia no centro cirúrgico.</p> <p>Portanto, "A Aventura de Lucca no Hospital Fundo do Mar" se revela uma ferramenta útil para a adaptação das crianças ao ambiente hospitalar. Ao transformar a experiência de internação em uma jornada familiar e compreensível, a animação contribui para a redução da ansiedade e do medo, criando um ambiente mais acolhedor e promovendo uma experiência hospitalar mais positiva e tranquila para os pequenos pacientes.</p> <p> </p>
Table 1 in Tertiary hospital sewage as reservoir of bacteria expressing MDR phenotype in Brazil
<p><b>Table 1.</b> Antibiotic resistance profile and multiresistant phenotype of bacteria isolated in sewage from a tertiary hospital located in Ribeirão Preto, São Paulo, Brazil.</p><table><tbody><tr><th><b>Sampling point</b></th><th><b>Species</b></th><th><b>Antibiotic resistance profile*</b></th><th><b>Resistance phenotype</b></th></tr></tbody><tbody><tr><th>Ambulatory</th><td><i>Proteus mirabilis</i></td><td>AMO, AMC, AMP, CAZ, ERT, SXT, TET</td><td>MDR</td></tr><tr><th>care</th><td><i>Yersinia enterocolitica</i></td><td>AMO, AMC, AMP, CTX, CFO, SXT</td><td>MDR</td></tr><tr><th></th><td><i>Enterococcus faecalis</i></td><td>CIP, CLI, GEN, SXT, TET, VAN</td><td>MDR</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>AMO, AMP, CAZ, SXT, TET</td><td>MDR</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>AMO, AMP</td><td>-</td></tr><tr><th></th><td><i>Klebsiella pneumoniae</i></td><td>AMO, AMP</td><td>-</td></tr><tr><th>Patient wards</th><td><i>Enterobacter cloacae</i> Complex</td><td>AMI, AMP, ASB, CPM, CFO, CAZ, CRO, CRX, CIP, ERT, GEN, IPM, MER, TZP</td><td>MDR</td></tr><tr><th></th><td><i>Klebsiella ozaenae</i></td><td>AMO, AMP, CTX, CFO, CAZ, CIP, ERT, GEN, IPM, MER, SXT, TET</td><td>MDR</td></tr><tr><th></th><td><i>Hafnia alvei</i></td><td>AMO, AMC, AMP, CTX, CFO, CAZ, ERT, GEN, IPM, MER, SXT</td><td>MDR</td></tr><tr><th></th><td><i>Klebsiella ozaenae</i></td><td>AMO, AMC, AMP, CTX, CFO, CAZ, CIP, ERT, IPM, MER, SXT</td><td>MDR</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>AMO, AMC, AMP, CTX, CAZ, ERT, IPM, MER, TET</td><td>MDR</td></tr><tr><th></th><td><i>Staphylococcus aureus</i></td><td>AMP, CIP, CLI, GEN, SXT, TCP, TET, VAN</td><td>MDR</td></tr><tr><th></th><td><i>Citrobacter youngae</i></td><td>AMO, AMC, AMP, CFO</td><td>MDR</td></tr><tr><th></th><td><i>Klebsiella pneumoniae</i></td><td>AMO, AMC, AMP, SXT</td><td>-</td></tr><tr><th></th><td><i>Enterococcus gallinarum</i></td><td>CLI, SXT, TET, VAN</td><td>-</td></tr><tr><th></th><td><i>Pseudomonas aeruginosa</i></td><td>CAZ, CIP, GEN</td><td>MDR</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>AMO, AMP</td><td>-</td></tr><tr><th></th><td><i>Pseudomonas aeruginosa</i></td><td>CAZ</td><td>-</td></tr><tr><th>Confluent point</th><td><i>Klebsiella pneumoniae</i></td><td>AMP, ASB, CPM, CFO, CAZ, CRO, CRX, CIP, ERT, GEN, IPM, MER, TZP</td><td>MDR</td></tr><tr><th></th><td><i>Hafnia alvei</i></td><td>AMO, AMC, AMP, CTX, CFO, CAZ, ERT, IPM, MER, SXT</td><td>MDR</td></tr><tr><th></th><td><i>Serratia liquefaciens</i></td><td>AMO, AMP, CTX, CFO, CAZ, ERT, IPM, MER</td><td>MDR</td></tr><tr><th></th><td><i>Pseudomonas aeruginosa</i></td><td>AMI, AMP, ASB, CPM, CFO, CAZ, CRO, CRX</td><td>-</td></tr><tr><th></th><td><i>Klebsiella pneumoniae</i></td><td>AMO, AMP, CTX, SXT, TET</td><td>MDR</td></tr><tr><th></th><td><i>Hafnia alvei</i></td><td>CTX, CAZ, ERT, IPM, MER</td><td>-</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>CFO, CAZ, TET</td><td>MDR</td></tr><tr><th></th><td><i>Klebsiella pneumoniae</i></td><td>AMO, AMP, CAZ</td><td>-</td></tr><tr><th></th><td><i>Enterococcus faecium</i></td><td>CLI, SXT</td><td>-</td></tr><tr><th></th><td><i>Escherichia coli</i></td><td>AMP</td><td>-</td></tr></tbody></table>
Simulated Object-Centric Event Logs (OCEL 2.0) for Order-to-Cash, Procure-to-Pay, Hiring, and Hospital Patient Lifecycle Processes
<p>This dataset contains simulated object-centric event logs for four distinct business processes: <strong>Order-to-Cash (O2C)</strong>, <strong>Procure-to-Pay (P2P)</strong>, <strong>Hiring</strong>, and <strong>Hospital Patient Lifecycle</strong>. Each process is designed to reflect realistic workflows, encompassing multiple object types and capturing key activities, decision points, and process dynamics. The dataset is aimed at providing a rich source of data for process mining, analysis, and modeling activities.</p> <p>1. <strong>Order-to-Cash (O2C)</strong>:<br> The O2C process simulates an end-to-end business flow starting from customer order placement to payment receipt. It includes diverse activities such as order approval, fulfillment, invoice generation, and payment processing, involving object types like Customers, Orders, Products, and Invoices. The dataset captures variability through random decisions, synchronization between departments, and workarounds in credit checks and inventory adjustments. Attributes such as customer tiers, order values, and shipment statuses add further depth, allowing for detailed analysis of this complex process.</p> <p>2. <strong>Procure-to-Pay (P2P)</strong>:<br> The P2P process simulates the procurement lifecycle, from requisition creation to payment of suppliers. Key activities include purchase order creation, three-way matching, goods receipt, and payment processing. The event log records object types such as Purchase Requisitions, Purchase Orders, Suppliers, and Invoices. Variability is introduced through approval decisions, batching, and potential mismatches in the matching process. The dataset represents the inherent complexities of real-world procurement operations, including batching and synchronization issues between different process stages.</p> <p>3. <strong>Hiring Process</strong>:<br> The hiring process log tracks the recruitment lifecycle, from job requisition creation to onboarding. It includes object types like Candidates, Job Requisitions, Recruiters, and Interviewers. The process covers activities such as resume screening, interviews, assessments, and offer management. Variability in the hiring process is introduced through random delays, candidate decisions, and background check durations. Batching occurs in stages like resume screening and onboarding, while synchronization challenges arise during interview scheduling.</p> <p>4. <strong>Hospital Patient Lifecycle</strong>:<br> This log represents the lifecycle of patients within a hospital, capturing interactions with multiple resources such as physicians, beds, and medical equipment. The process begins with pre-admission activities, followed by diagnosis, treatment, and discharge. The dataset includes object types like Patients, Physicians, and Medical Equipment, with attributes related to patient demographics and event severity. The process reflects the dynamic nature of hospital operations, including synchronization of resources and the occurrence of workarounds in case of delays or resource unavailability.</p> <p>Each process simulation captures high variability, synchronization issues, and batching, making this dataset suitable for analyzing real-world operational challenges. The logs provide a comprehensive view of complex workflows, supporting advanced analysis, including object-centric process mining.</p> <p>This description will provide the necessary details about the dataset, highlighting its structure, purpose, and potential uses for researchers and process analysts.</p> <p>Object-centric event logs conceived and simulated by the <strong>o1-preview-2024-09-12</strong> LRM, using the https://github.com/fit-alessandro-berti/llm-ocel-simulator project.</p> <p> </p> <p> </p>
Acid blood gases including fetal hemoglobin from newborns, 2019-2023, General University Hospital in Prague
<p>This anonymized data is from the e-health database of the General University Hospital. It has blood gases that include the parameter of fetal hemoglobin percentage.</p> <p>The variables are as follows</p> <p>birthweight - birthweight</p> <p>EGA - gestational age in decimals</p> <p>day_of_life - postnatal day of life when the sample was taken</p> <p>blood_type - source of blood sample (a - arterial, c - capillary, v - venous)</p> <p>nr_of_preceding_trf - the amount of transfusions the patient has received preceding this blood sample</p> <p>hb - hemoglobin (g/l)</p> <p>F-hbf - fetal hemoglobin percentage</p> <p>pco2 - partial pressure of carbon dioxide (kPa)</p> <p>po2 - partial pressure of oxygen (kPa)</p> <p>so2 - oxygen saturation (ratio of oxyhemoglobin to total)</p> <p>pH - pH</p> <p>PO2 - partial pressure of oxygen (mmHg)</p> <p>estPaO2 - estimated partial pressure of oxygen at the given oxygen saturation according to the Severinghaus equation (mmHg)</p> <p>PaO2-extPO2 - difference between actual measured and estimated partial pressure of oxygen at the given oxygen saturation</p> <p>PCO2 - partial pressure of carbon dioxide (mmHg)</p>
Data from: Discharge communication for chronic disease patients in three hospitals in India
OBJECTIVES <p>Poor discharge communication is associated with negative health outcomes in high-income countries. However, quality of discharge communication has received little attention in India and many other low and middle-income countries. Primary Objective To investigate verbal and documented discharge communication for chronic non-communicable disease (NCD) patients. Secondary objective To explore the relationship between quality of discharge communication and health outcomes.</p> METHODS: <p>Design Prospective study.</p> <p><strong>Setting:</strong> Three public hospitals in Himachal Pradesh and Kerala states, India.</p> <p><strong>Participants:</strong> 546 chronic NCD (chronic respiratory disease, cardiovascular disease or diabetes) patients. Piloted questionnaires were completed at admission, discharge and Five and eighteen-week follow-up covering health status, health-seeking behaviour and healthcare information exchange practices. Logistic regression was used to explore the relationship between quality of discharge communication and health outcomes.</p> <p><strong>Outcome Measures:</strong></p> <p><strong>Primary:</strong> Patient recall and experiences of verbal and documented discharge communication.</p> <p><strong>Secondary:</strong> Death, hospital readmission and self-reported deterioration of NCD/s.</p> RESULTS <p>All patients received discharge notes, which were predominantly on minimally structured sheets of paper (71%); 31% of notes contained all of the following information required for facilitating continuity of care: diagnosis, medication information, lifestyle advice, and follow-up instructions. Patient reports indicated notable variations in verbal information provided during discharge consultations; 50% received ongoing treatment/management information and 23% received lifestyle advice. Within 18 weeks of follow-up, 25 (5%) patients had died, 69 (13%) had been readmitted and 62 (11%) reported that their chronic NCD/s had deteriorated. Significant associations were found between low-quality documented discharge communication and death (AOR=3.00; 95% CI 1.27,7.06) and low-quality verbal discharge communication and self-reported deterioration of chronic NCD/s (AOR=0.46; 95% CI 0.25,0.83) within 18-weeks of follow-up.</p> CONCLUSIONS <p>Sub-optimal discharge practices may be compromising the continuity and safety of chronic NCD patient care. Structured protocols, documents and training are required to improve discharge communication, healthcare integration and overall NCD management.</p>
A multi-modal sensor dataset for continuous stress detection of nurses in a hospital
<p>Advances in wearable technologies provide the opportunity to monitor many physiological variables continuously. Stress detection has gained increased attention in recent years, especially because early stress detection can help individuals better manage health to minimize the negative impacts of long-term stress exposure. This paper provides a unique stress detection dataset created in a natural working environment in a hospital. This dataset is a collection of biometric data of nurses during the COVID-19 outbreak. Studying stress in a work environment is complex due to the influence of many social, cultural, and individuals experience in dealing with stressful conditions. In order to address these concerns, we captured both the physiological data and associated context pertaining to the stress events. We monitored specific physiological variables, including electrodermal activity, heart rate, skin temperature, and accelerometer data of the nurse subjects. A periodic smartphone-administered survey also captured the contributing factors for the detected stress events. A database containing the signals, stress events, and survey responses is available upon request.</p>
Indirect effect of 7-valent and 13-valent pneumococcal conjugated vaccines on pneumococcal pneumonia hospitalizations in elderly
<p>Data base and data dictionary used in the study of indirect effect of 7-valent and 13-valent pneumococcal conjugated vaccines on pneumococcal pneumonia hospitalizations in elderly</p>
Risk of Dementia and Its Associated Factors Among the Patients with Coronary Artery Disease Attending a Tertiary Cardiac Hospital of Dhaka City: A Cross-sectional Study
<p>This data set of research assessed the risk of dementia among patients with coronary artery disease.</p>
Time-to-Event analysis of factors influencing delay in discharge from a subacute Complex Discharge Unit during the first year of the pandemic (2020) in an Irish tertiary centre hospital
<p><strong>Figure S1:</strong> Forest plots 1 and 2 depicting Age and Gender strata associated Hazard ratio (Markers) estimates (95% Confidence Interval demonstrated by horizontal line) exhibited statistically significant results for individuals <65 years of age who had a delay in discharge due to complications from comorbidities; those in 65-75 years of age category, had prolonged LOS due to admission with frailty, falls and/or integrated rehabilitation needs; and 75-85 years of age category showed an association of at least 4 out of the 5 common delaying factors. Strata Gender exhibited a significant delay in discharge due to complications from comorbidities and patient-centred needs; in comparison to the female gender who also experienced a delay in discharge as a result of both factors alongside frailty, falls and/or integrated rehabilitation needs.<strong>[A. </strong>Complications/comorbidities prolonging discharge, <strong>B.</strong> Healthcare-associated infection, <strong>C</strong>. Frailty, falls and/or integrated rehabilitation needs, <strong>D</strong>. Patient-centred needs, <strong>E</strong>. Community services]. </p> <p><strong>Figure S2:</strong> Forest plot 3 depicting Multimorbidity (MM) strata-associated Hazard ratio (Markers) estimates (95% Confidence Interval demonstrated by horizontal line) exhibited a significant delay in discharge due to complications from comorbidities, frailty, falls, and/or integrated rehabilitation and patient-centred needs in patients with ≤4 MM. In contrast patients with >4 MM experienced significant delays in discharge due to complications from comorbidities and patient-centred needs. <strong>[A. </strong>Complications/comorbidities prolonging discharge, <strong>B.</strong> Healthcare-associated infection, <strong>C</strong>. Frailty, falls and/or integrated rehabilitation needs, <strong>D</strong>. Patient-centred needs, <strong>E</strong>. Community Services].</p>
Health records from hospitalized adults with malaria during 2019 at Bo Government Hospital, Sierra Leone
<p class="MsoNormal"><span>We performed a chart review of adults admitted to Bo Government Hospital during 2019. Of 893 admissions,149 (59% female, mean age 58.5 years) had a laboratory diagnosis of malaria and 22 (14.8%) died. Mortality was significantly higher among patients with severe malaria compared with those who had non-severe malaria (6/20 [30%] versus 16/129 [12.4%], <em>p</em>=0.031). Our data suggest that malaria is a common cause of death in hospitalized Sierra Leonean adults. </span></p>
ANION GAP OR SERUM LACTATE-IN SEARCH OF A BETTER PROGNOSTIC MARKER IN SEPSIS A CROSS-SECTIONAL STUDY IN A RURAL TERTIARY CARE HOSPITAL
<p>master data sheet</p>
qechairquality: Air Quality Data at Queen Elizabeth Central Hospital (QECH), Blantyre, Malawi.
<p>Air quality data with measurements in 5-minute intervals for particulate matter (PM 2.5 & PM 10) at eight sensor locations over two months at Queen Elizabeth Central Hospital (QECH) in Blantyre, Malawi.</p>
How do patients sleep after orthopedic surgery? Changes in objective sleep parameters and pain in hospitalized patients undergoing hip and knee arthroplasty
<p><strong>Aim.</strong> Sleep impairment after hip and knee arthroplasty is multifactorial and still poorly understood and post-operative pain is a potential cause of sleep deprivation or disturbances. The aim of this study was to assess actigraphy-based sleep characteristics and pain scores in patients undergoing knee or hip joint replacement and hospitalized for ten days after surgery. <strong>Methods.</strong> In this observational cohort study, n=20 subjects (11 males and 9 females; age: 64.0±10.39 years old) completed a daily sleep diary and wore the Actiwatch 2 actigraph (Philips Respironics, Portland, OR) to record sleep parameters for 11 consecutive days, starting the day before surgery and ending the 10<sup>th</sup> day after surgery. Subjective scores of pain, by a visual analog scale (VAS), were constantly monitored for the entire experimental protocol. The following evaluation time-points have been considered for the analysis: pre-surgery (PRE), the first (POST1), the fourth (POST4) and the tenth day (POST10) after surgery. A repeated-measures one-way analysis of variance followed by the Tukey-Kramer post hoc tests, or the equivalent nonparametric Friedman test followed by the Dunn’s multiple comparisons, was applied to test differences in sleep and pain among time-points. Test for correlation between mean VAS score and the sleep parameters at each post-operative time point were performed using Pearson’s method. <strong>Results.</strong> Sleep quantity and timing parameters did not differ from PRE to POST10, during the hospitalization. On the contrary, Sleep Efficiency (p=0.006; η<sup>2</sup><sub>p</sub>=0.45, large), Sleep Latency (p=0.039; η<sup>2</sup><sub>p</sub>=0.37, large), Wake After Sleep Onset (p=0.042; η<sup>2</sup><sub>p</sub>=0.34, large), Immobility Time (p=0.005; η<sup>2</sup><sub>p</sub>=0.47, large) and Fragmentation Index (p=0.016; η<sup>2</sup><sub>p</sub>=0.36, large) displayed significant differences. In detail, Sleep Efficiency and Immobility Time significantly decreased at POST1 compared to PRE by 10.8% (p=0.003; ES: 0.9, moderate) and 9.4% (p=0.005; ES: 0.86, moderate) respectively. Overall, all sleep quality parameters showed a trend of constant improvement from POST1 to POST10. VAS scores were higher in the 1<sup>st</sup> day post-surgery (4.58 ± 2.46; p=0.0011 and ES: 1.40, large) compared to POST10 (1.68 ± 1.58) and in POST4 (3.85 ± 2.31) compared to POST10 (-2.18; p=0.0087 and ES: 1.09, moderate). During time, mean VAS showed significant negative correlations with mean Sleep Efficiency (r = -0.71; p=0.021) and mean Immobility Time (r = - 0.83; p=0.003) and a significant positive correlation with Fragmentation Index (r = 0.70; p=0.023), highlighting that high scores of pain were associated with lower overall sleep quality. <strong>Conclusion.</strong> Sleep quantity and timing parameters were stable during the entire hospitalization whereas sleep quality parameters significantly worsened the 1<sup>st</sup> night after surgery compared to the pre-surgery night and, overall, these parameters showed a trend of constant improvement until discharge. Pain scores were at the highest level the 1<sup>st</sup> day after surgery and at the lowest level before discharge with a negative correlation between pain and sleep quality parameters during time. The present study confirms the important impact of surgery on sleep parameters, especially on the first few post-operative days.</p>
Development of a Predictive Model for In-Hospital Mortality in COVID-19 Patients Using CAR, IL-6, IL-6/LY, and NLR: A Single-Center Study in Indonesia
<p>Figure 1. ROC Curve of CAR, IL-6, IL-6/LY, and NLR</p> <p> </p> <p>Figure 2. Kaplan Meier curve of (a) CAR (b) IL-6 (c) IL-6/LY (d) NLR blue line represents group above cut off and green one represents group below cut-off</p> <p> </p>
Data from: Plasma proteomic signatures of enteric permeability among hospitalized and community children under two years of age in Kenya and Pakistan
<p class="MsoNormal">We aimed to establish if enteric permeability was associated with similar biological processes in children recovering from hospitalization and relatively healthy children in the community. Extreme gradient-boosted models predicting the lactulose rhamnose ratio, a biomarker of enteric permeability, using 7,500 plasma proteins and 34 fecal biomarkers of enteric infection among 89 hospitalized and 60 community children aged 2-23 months were built. The R<sup>2</sup> were calculated in test sets. The models performed better among community (R<sup>2</sup>: 0·27 [min-max: 0·19, 0·53]) than hospitalized children (R<sup>2</sup>: 0·07 [min-max: 0·03, 0·11]). In the community, LRR was associated with biomarkers of humoral antimicrobial and cellular lipopolysaccharide responses, and inversely associated with anti-inflammatory and innate immunological responses. Among hospitalized children, the selected biomarkers had few shared functions.<strong> </strong>This suggests enteric permeability among community children was associated with a host response to pathogens, but this association was not observed among hospitalized children.</p>
STROBE checklist_Study of compliance to treatment advised to patients attending medicine OPD in a tertiary care ruraral hospital
<p>STROBE checklist of the study titled, "Study of compliance to treatment advised to patients attending medicine OPD in a tertiary care ruraral hospital."</p>
Compliance worksheet_Study of compliance to treatment advised to patients attending medicine OPD in a tertiary care rural hospital
<p>This is a worksheet of the data obtained from the questionnaires used for the study titled, "Study of compliance to treatment advised to patients attending medicine OPD in a tertiary care rural hospital."</p>
Assessment of the Quality of Life and Resilience in Ventilated Patients with COVID-19: Findings One year After Hospital Discharge
<p>Assessment of the Quality of Life and Resilience in Ventilated Patients with COVID-19: Findings One year After Hospital Discharge</p> <p>This dataset was collected in a prospective cohort study to evaluate the quality of life and resilience of patients managed with invasive mechanical ventilation due to severe COVID-19, one year after hospital discharge.</p> <p><strong>Methodology</strong></p> <p><strong>Design </strong></p> <p>A prospective cohort study was conducted, and the present study received approval from the Ethics Committee of Universidad del Rosario (DVO005-1957- CV1534). Informed consent was obtained from each patient or their legal caregiver, which was given via telephone.</p> <p><strong>Patients/Population</strong></p> <p>Data were collected from patients who received MV support for respiratory failure secondary to critical illness due to COVID-19. The study included consecutively patients discharged alive from the Hospital Universitario Mayor Méderi located in the city of Bogota, Colombia from March 19, 2020, to April 30, 2021. Patients who passed away after discharge, those with whom telephone communication was not feasible, individuals with cognitive impairment preventing them from responding to the survey without obtaining information from a caregiver, and those who declined to participate in the study (as indicated by the patient and/or caregiver) were excluded from the final analysis.</p> <p><strong>Intervention/Measurement</strong></p> <p>Participants were administered a structured questionnaire that included three scales: PCFS (Post-COVID-19 functional Status Scale), EQ-5D-3L (EuroQol 5 dimensions 3 levels) (administered via telephone application), and CD-RISC (Connor-Davidson Resilience Scale). Permission for the non-commercial use of the EQ-5D-3L scale was obtained from the EuroQol Customer Portal (Registration No. 55265).</p> <p><strong>Statistical Analysis</strong></p> <p>The general characteristics of the population and the results of the PCFS, EQ-5D-3L, and CD-RISC were described. Continuous variables were presented as mean and standard deviation (SD) or median and interquartile range (IQR) depending on the data distribution. Categorical variables were described using absolute and relative frequencies.</p> <p>The PCFS was reported using a grading system ranging from Grade 0, indicating patients who were able to resume their daily activities without limitations, to Grade 4, representing severe functional limitations.</p> <p>The five dimensions of the EQ-5D-3L were considered as numerical variables and scored as follows: 1 for no alteration, 2 for moderate alteration, and 3 for severe alteration. The total score was computed by summing the scores from each dimension, ranging from 5 (indicating optimal quality of life) to 15 (indicating severely compromised quality of life). To assess the usefulness of the PCFS in measuring QoL, Pearson correlations were performed between the total score of the EQ-5D-3L and each individual dimension with the PCFS score.</p> <p>The CD-RISC and its 25 dimensions were evaluated as well. Each dimension was treated as a numerical variable and scored as follows: 0 for no alteration, 1 for mild alteration, 2 for moderate alteration, 3 for severe alteration, and 4 for critical alteration. The total CD-RISC score ranged from 0 (indicating no resilience) to 100 (indicating the best possible resilience). Furthermore, it was hypothesized that patients with higher resilience, as measured by the CD-RISC scale, would have better quality of life (QoL). Pearson correlations were examined between the PCFS score and the total CD-RISC score.</p>
ScienceDex guides
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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.