Tag: statistics

Int J Orthop Trauma Nurs. 2025 Jun 16;58:101208. doi: 10.1016/j.ijotn.2025.101208. Online ahead of print.

ABSTRACT

AIM: The research was conducted to identify the discharge education needs of patients undergoing day surgery for hand surgery.

METHOD: This descriptive and cross-sectional study included patients who underwent day surgery for hand conditions, both emergency and elective, over the course of a year at public hospitals under the Istanbul Provincial Health Directorate, specifically in the Department of Hand Surgery on the European side of Istanbul. The total patient count was 1195. The study sample consisted of 100 patients, determined using the G Power analysis method with an effect size of 2.25, a significance level (α) of 0.05, and a power of 90 %. Data were collected using a “Patient Identification Form” and “The Patient Learning Needs Scale,” both developed by the researcher.

RESULTS: The average age of the patients participating in the study was 41.59 ± 15.53, with the majority being male (55.39 %), married (62.31 %), unemployed (53.85 %), and without chronic diseases (75.38 %). The average score of the “Patient Learning Needs Scale” for day surgery hand patients was 156.461 ± 12.133, with the highest average scores in the sub-dimensions “Treatment and Complications” being 32.62 ± 3.75. A significant relationship was found between age and the “Treatment and Complications” sub-dimension of the PLNS, with a correlation of 0.103 (r = 0.103; p < 0.05). It was determined that male patients had greater learning needs regarding skin care (p < 0.05), and statistically significant differences were found among the educational levels in the sub-dimensions of “Activities of Daily Living” “Community and Follow-up,” and “Skin Care” of the PLNS (p < 0.05). The educational needs of employed patients were higher in all sub-dimensions except “Treatment and Complications” and the total score of the scale, with a statistically significant difference found in the “Situation-Related Emotions” sub-dimension. Patients who applied to the emergency department had a higher average total score on the PLNS.

CONCLUSION: It has been determined that patients undergoing day surgery for hand surgery have educational needs regarding treatment and complications, medications, skin care, quality of life, and home care during the discharge process. It is recommended that discharge education programs be increased to consider the needs of the patients.

PMID:40554831 | DOI:10.1016/j.ijotn.2025.101208

Comput Methods Programs Biomed. 2025 Jun 18;269:108898. doi: 10.1016/j.cmpb.2025.108898. Online ahead of print.

ABSTRACT

BACKGROUNDS AND OBJECTIVES: Cardiac arrhythmias, characterized by irregular heartbeats, are difficult to diagnose in real-world scenarios. Machine learning has advanced arrhythmia detection; however, the optimal number of heartbeats for precise classification remains understudied. This study addresses this using machine learning while assessing the performance of arrhythmia detection across inter-patient and intra-patient conditions. Furthermore, the performance-resource trade-offs are evaluated for practical deployment in mobile health (mHealth) applications.

METHODS: Beat-wise segmentation and resampling techniques were utilized for preprocessing electrocardiography (ECG) signals to ensure consistent input lengths. A 1-D convolutional neural network was used to classify the eight multi-labeled arrhythmias. The dataset comprised real-world ECG recordings from the HiCardi wireless device alongside data from the MIT-BIH Arrhythmia database. Model performance was assessed through fivefold cross-validation under both inter-patient and intra-patient conditions.

RESULTS: The proposed model demonstrated peak accuracy at four beats under inter-patient conditions, with minimal improvements beyond this point. This configuration achieved a balance between performance (94.82% accuracy) and resource consumption (training time: 72.27 s per epoch; prediction time: 155 μs per segment). Real-world simulations validated the feasibility of real-time arrhythmia detection for approximately 5000 patients.

CONCLUSION: Utilizing four heartbeats as the input size for arrhythmia classification results in a trade-off between accuracy and computational efficiency. This discovery has significant implications for real-time wearable ECG devices, where both performance and resource constraints are crucial considerations. This insight is expected to serve as a valuable reference for enhancing the design and implementation of arrhythmia detection systems for scalable and efficient mHealth applications.

PMID:40554829 | DOI:10.1016/j.cmpb.2025.108898

Comput Biol Chem. 2025 Jun 16;119:108550. doi: 10.1016/j.compbiolchem.2025.108550. Online ahead of print.

ABSTRACT

BACKGROUND: In contemporary context, epileptic seizure stands as the most prevalent neurological disorder arising from the sudden atypical release of brain neurons and after effect of stress. The electroencephalogram (EEG) has been widely employed for epilepsy detection. Noteworthy are the Deep Learning methods and signal processing techniques utilized for seizures classification and detection. Through advancements in Deep Learning within the biomedical domain, several methodologies have been applied to identify and forecast seizure occurrences based on EEG data collected from individuals with epilepsy, typically confined from temporary in medical screening with standard scalp-EEG or intra-cerebral electrodes.

PURPOSE: This work aims to generate a mechanism for seizure recognition from EEG signals using a classification technique based on Deep Learning. The initial phase involves pre-processing, where denoising of input EEG signals is performed by employing the Short-Time Fourier Transform (STFT). Consequently, time-domain, spectral, and statistical features were extracted from pre-processed signals.

METHODS: Then, feature selection is performed utilizing Fractional Adadelta Chameleon Swarm Algorithm (FrAdadelta-CSA), a method that integrates the notion of fractional calculus into Adadelta Chameleon Swarm Algorithm (Adadelta-CSA). Finally, seizure prediction is conducted based on the selected features using SpikeGoogle-DenseNet, a hybrid model of SpikeGoogle and DenseNet.

RESULTS AND CONCLUSION: Experimental outcomes reveal that the proposed method achieved an accuracy of 96.2 %, sensitivity of 97.3 %, and specificity of 94.5 %.

PMID:40554820 | DOI:10.1016/j.compbiolchem.2025.108550

Res Vet Sci. 2025 Jun 19;193:105777. doi: 10.1016/j.rvsc.2025.105777. Online ahead of print.

ABSTRACT

This study investigated the presence of δ bovine papillomaviruses (BPV-1, BPV-2, BPV-13, BPV-14) in equine skin lesions from Southern Italy, focusing on equine sarcoids and their histopathological correlations. 63 equine skin samples were analysed using PCR and sequencing for BPV detection, and their histopathological features were assessed. BPV DNA was detected in 69.84% of the samples, with BPV-1 and BPV-2 being the most prevalent genotype, followed by BPV-13, while BPV-14 was not detected. BPV DNA was also found in non-sarcoid tumours and non-neoplastic conditions. Histopathological analysis revealed in 41 samples typical sarcoid features: fibroblastic atypia and extracellular matrix deposition. Despite no statistical correlation was found between BPV genotypes and histopathological features, BPV-1 infection was associated with more severe fibroblastic atypia and abundant extracellular matrix. This study provides insights into the prevalence and potential pathogenic roles of different BPV genotypes in equine sarcoids and other skin lesions, underscoring the critical need for further research to develop targeted therapies.

PMID:40554817 | DOI:10.1016/j.rvsc.2025.105777

JMIR Form Res. 2025 Jun 24;9:e70853. doi: 10.2196/70853.

ABSTRACT

BACKGROUND: The common phrase “representation matters” asserts that media has a measurable and important impact on civic society’s perception of self and others. The representation of health in media, in particular, may reflect and perpetuate a society’s disease burden.

OBJECTIVE: In this study, for the top 10 major causes of death in the United States, we aimed to examine how cinematic representation overall and by-gender mortality diverges from reality.

METHODS: Using crowd-sourced data on over 68,000 film deaths from Cinemorgue Wiki, we employ natural language processing techniques to analyze shifts in representation of deaths in movies versus the 2021 National Vital Statistics Survey top 10 mortality causes. We parsed, stemmed, and classified each film death database entry, and then categorized film deaths by gender using a specifically trained gender text classifier.

RESULTS: Overall, movies strongly overrepresent suicide and, to a lesser degree, accidents. In terms of gender, movies overrepresent men and underrepresent women for nearly every major mortality cause, including heart disease and cerebrovascular disease (chi-square test, P<.001); 73.6% (477/648) of film deaths from heart disease were men (vs 384,866/695,547, 55.4% in real life) and 69.4% (50/72) of film deaths from cerebrovascular disease were men (vs 70,852/162,890, 43.5% in real life). The 2 exceptions for which women were overrepresented are suicide and accidents (chi-square test, P<.001), with 39.7% (945/2382) deaths from suicide in film being women (vs 9825/48,183, 20.4% in real life) and 38.8% (485/1250) deaths from accidents in film being women (vs 75,333/225,935, 33.5% in real life).

CONCLUSIONS: We discuss the implications of under- and overrepresenting causes of death overall and by gender, as well as areas of future research.

PMID:40554798 | DOI:10.2196/70853

JMIR Res Protoc. 2025 Jun 24;14:e64405. doi: 10.2196/64405.

ABSTRACT

BACKGROUND: Black and Latino men are at increased risk for poor diabetes health outcomes but are underrepresented in lifestyle interventions for weight loss and diabetes prevention. Although relatively few men participate in the National Diabetes Prevention Program (NDPP), it remains the most widely available evidence-based approach to type 2 diabetes prevention in the United States. Thus, an NDPP tailored to Black and Latino men has the potential to address prior limitations of NDPP implementation and reduce gender, racial, and ethnic diabetes disparities. It also provides an opportunity to define a population for targeted outreach and evaluate the reach of our recruitment methods and interventions.

OBJECTIVE: We tailored the US Centers for Disease Control and Prevention Prevent T2 curriculum for the NDPP for Black and Latino men, called Power-Up, and will evaluate its effects in comparison to standard mixed-gender NDPP groups via virtual delivery. The primary aim of the project is to assess the effect of Power-Up versus NDPP on weight loss among men with prediabetes. The secondary aim is to compare the engagement and retention of men with prediabetes in Power-Up versus NDPP. We will also examine the reach of our recruitment methods and engagement in our screening, consenting, and assessment procedures prior to the point of randomization. We hypothesized that men randomized to Power-Up would achieve greater percent weight loss from baseline at 16 weeks (end of Core sessions) and 1 year (end of Maintenance sessions) than men randomized to standard, mixed-gender NDPP. Power-Up is also expected to have better engagement and retention.

METHODS: Using the electronic health record (EHR) systems of a large academic medical center and a network of small to medium independent primary care practices throughout New York City, we identified Black and Latino men who met eligibility criteria for NDPP and enrolled them in a randomized controlled trial in which they were assigned 1:1 to receive Power-Up or the standard, mixed-gender NDPP over 1 year via online videoconferencing. Coaches delivering these interventions were trained according to the standards for the NDPP. Power-Up will be delivered by men coaches. Weight will be collected with home-based electronic scales for primary outcome analyses. Engagement will be assessed by session attendance logs.

RESULTS: We identified 11,052 men for outreach based on EHR data, successfully screened 26% of them, consented and enrolled 22% of these, and randomly assigned 48% of consented participants. Primary and secondary outcome analyses will be assessed among randomized men.

CONCLUSIONS: This study highlights the effort required to reach and engage Black and Latino men for virtually delivered diabetes prevention programs. Forthcoming trial results for weight loss and engagement will further inform efforts to address disparities in diabetes prevention through tailored programming for Black and Latino men.

TRIAL REGISTRATION: ClinicalTrials.gov NCT04104243; https://clinicaltrials.gov/study/NCT04104243.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64405.

PMID:40554781 | DOI:10.2196/64405

JMIR Form Res. 2025 Jun 24;9:e65478. doi: 10.2196/65478.

ABSTRACT

BACKGROUND: Mental health disorders affect 1 in 8 people worldwide, yet many face barriers to accessing care. E-mental health interventions, including self-guided internet-based programs, offer promising solutions. However, the mechanisms driving knowledge gain in such programs remain poorly understood. The role of medium, topic, sequence, and confidence and their interaction in learning outcomes need further investigation. Additionally, the influence of knowledge gaps on the outcome of psychoeducational intervention is not well understood (eg, whether psychoeducation requires an existing knowledge gap to be effective).

OBJECTIVE: This randomized controlled trial investigated the role of medium, topic, sequence, and participants’ initial knowledge levels on knowledge gain and confidence in fully automated self-guided e-mental health psychoeducation.

METHODS: A total of 158 adults (mean age 34, SD 12.4 years; n=118, 74.7% female) were randomized to 8 experimental conditions (receiving video, texts, or both containing psychoeducational content on sleep or social competence; n=142) or a control group (neutral video; n=16). The fully automated interventions (videos) were developed for use in web-based e-mental health interventions. They address transdiagnostic symptoms and hence are relevant across various disorders. To assess the added value of video production for knowledge gain, text-based scripts corresponding to the video content were created and compared. All interventions and outcome assessments were delivered on the web via Qualtrics without face-to-face components. Pre- and postintervention knowledge was assessed using a validated 30-item knowledge test (true/false). Confidence in responses was rated on a 0% to 100% scale. Statistical analyses included 3-way ANOVA and multivariate ANOVA.

RESULTS: Knowledge significantly increased across experimental groups (F_1,156=17.272; P<.001; η_p²=0.10). Participants with social competence deficits had significantly lower baseline knowledge (P=.04; d=0.41). For sleep deficits, a nonsignificant trend emerged (P=.09; d=0.28). Participants with social competence deficits demonstrated greater knowledge improvement (t₁₄₁=7.12; P<.001; d=0.60). Participants with sleep deficits showed smaller but significant gains (t₁₄₁=2.43; P=.02; d=0.20). No significant differences in knowledge gain were found between video and text formats. Confidence in correct answers increased significantly in the experimental group (mean 42.82, 95% CI 41.15-44.50 to mean 51.67, 95% CI 49.28-54.04), with larger gains for social competence than sleep. Confidence in the control group remained unchanged.

CONCLUSIONS: Both video and text formats effectively facilitated knowledge gain in e-mental health interventions, with no clear advantage of one medium over the other. Participants with prior deficits learned more in areas where they initially lacked knowledge. Confidence in correct answers increased alongside knowledge, highlighting psychoeducation’s role in promoting self-efficacy. Future research should explore multimedia integration to enhance adherence and symptom improvement.

TRIAL REGISTRATION: German Clinical Trials Register DRKS00026722; https://drks.de/search/en/trial/DRKS00026722.

PMID:40554780 | DOI:10.2196/65478

JMIR Form Res. 2025 Jun 24;9:e59481. doi: 10.2196/59481.

ABSTRACT

BACKGROUND: Research dissemination is a vital step in bridging the gap between the publication of cutting-edge research and its adoption into clinical practice. Social media platforms like Twitter (rebranded as X) offer promising channels for dissemination, yet research organizations lack clear guidance on establishing a professional social media presence. We present a structured framework based on our research network’s multiyear experience developing a Twitter account for research dissemination.

OBJECTIVE: This study aimed to provide a roadmap for organizations aiming to create a professional Twitter account for research dissemination.

METHODS: This was a mixed methods study analyzing the Pediatric Emergency Care Applied Research Network (PECARN) Twitter team’s 4-year experience (2020-2023) with building a social media account. Using the nominal group technique qualitative approach, we recorded insights from the 6 team members’ experiences in a round-robin fashion until response saturation. In addition, we analyzed internal Slack (Slack Technologies) communications to identify key developmental events. Together, these were then prioritized by consensus to elucidate key developmental events that enhanced both social media and scientific engagement. This process was informed by quantitative data from Twitter performance metrics and Altmetric Attention Scores for journal publications collected over a 39-month period. Together, these elements informed the design of a logic model framework.

RESULTS: The nominal group technique generated 63 thematic statements which included issues such as organizational structure, content strategy, technologies, analytics, organizational priorities, and challenges. These statements coalesced into the 7 domains (priorities, assumptions, inputs, outputs, outcomes, and external factors) that comprise the logic model. Inputs included organizational support (eg, executive-level champion and funding), specialized personnel (eg, content writer and analytics manager), and operational technologies (eg, communications and data analytics tools). Outputs encompassed targeted activities, such as engaging with other Twitter accounts, publishing high-quality tweets highlighting scholarly work, and developing a dynamic operations manual for the Twitter team. Outcomes were measured through tweet metrics, account analytics, and article-level impact scores.

CONCLUSIONS: Our logic model roadmap, based on our practical multiyear experience and data-driven strategies, can serve as a guide for research organizations or medical institutions aiming to incorporate Twitter or other social media platforms for research dissemination.

PMID:40554778 | DOI:10.2196/59481

J Biol Dyn. 2025 Dec;19(1):2521509. doi: 10.1080/17513758.2025.2521509. Epub 2025 Jun 24.

ABSTRACT

This paper is concerned with a stochastic SEIR model with infectivity in the incubation period and homestead-isolation on the susceptible, which is perturbed by white and colour noises. The model has a unique stationary distribution, which reflects the persistence of epidemics over a long period. Using the Has-minskii theorem and constructing stochastic Lyapunov functions with regime switching, we derive an important condition R0s. Comparing the expression for R0 and R0s, we can see that if there is no environmental noise, then R0s=R0. It ensures the asymptotic stability of the positive equilibrium E∗ of the corresponding deterministic system.

PMID:40554776 | DOI:10.1080/17513758.2025.2521509

Pancreas. 2025 Jun 25. doi: 10.1097/MPA.0000000000002526. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate the contributing factors for the development of systemic inflammatory response syndrome (SIRS) in acute pancreatitis (AP) patients and subsequently develop a novel nomogram prediction model.

METHODS: A multivariate logistic regression analysis was conducted to determine independent predictors of SIRS, where the variables were chosen based on statistical significance from univariate analysis. Based on their presence, 238 AP patients were grouped into non-sIRS (n=170) and sIRS (n=68). Logistic regression analysis identified independent predictors of sIRS complications. We then developed a visual nomogram prediction model alongside a logistic regression model. The model’s predictive power cut-off was determined by receiver operating characteristic (ROC) curve analysis, providing sensitivity, specificity, and predictive accuracy.

RESULTS: The study found that in the cohort of acute pancreatitis (AP) patients, systemic inflammatory response syndrome (SIRS) incidence was 28.6%. From our analysis, we determined that red blood cell distribution width (RDW), fibrinogen (FIB), amylase (AMY), blood glucose (Glu), and lactate dehydrogenase (LDH) were independent risk factors for SIRS. Additionally, we calculated the area under the ROC curve (AUC) for our prediction model of SIRS reached 0.816, which exceeded the AUCs of the individual risk indicators (RDW, FIB, AMY, Glu, LDH) and the bedside index of severity in acute pancreatitis (BISAP) score. In addition, we conducted a correlation analysis to validate the relationships among the predictive factors and to eliminate possible multicollinearity. The calibration curve plot showed that the nomogram agreed well between the predicted SIRS and actual risks. Finally, the clinical decision curve for our model also indicated its clinical utility by guiding decision-making for timely interventions at a threshold probability range of 0.4 to 1.

CONCLUSION: The model predicted non-SIRS with a critical value ≥0.332, a sensitivity of 71.3% and specificity of 87.1%, and a Kappa value of 0.56. These results indicate that this prediction model is based on admission data, with recommended additional validation assessments at multiple time points (e.g., 24, 48, and 72 h) to characterize the progression of SIR’s risk fully. Overall, this nomogram prediction model provides an efficient and simple means to predict SIRS for patients with AP.

PMID:40554769 | DOI:10.1097/MPA.0000000000002526