Early versus delayed necrosectomy in pancreatic necrosis: A population-based cohort study on readmission, healthcare utilization, and in-hospital mortality.

BACKGROUND: Acute necrotizing pancreatitis is a severe and life-threatening condition. It poses a considerable challenge for clinicians due to its complex nature and the high risk of complications. Several minimally invasive and open necrosectomy procedures have been developed. Despite advancements in treatment modalities, the optimal timing to perform necrosectomy lacks consensus. AIM: To evaluate the impact of necrosectomy timing on patients with pancreatic necrosis in the United States. METHODS: A national retrospective cohort study was conducted using the 2016-2019 Nationwide Readmissions Database. Patients with non-elective admissions for pancreatic necrosis were identified. The participants were divided into two groups based on the necrosectomy timing: The early group received intervention within 48 hours, whereas the delayed group underwent the procedure after 48 hours. The various intervention techniques included endoscopic, percutaneous, or surgical necrosectomy. The major outcomes of interest were 30-day readmission rates, healthcare utilization, and inpatient mortality. RESULTS: A total of 1309 patients with pancreatic necrosis were included. After propensity score matching, 349 cases treated with early necrosectomy were matched to 375 controls who received delayed intervention. The early cohort had a 30-day readmission rate of 8.6% compared to 4.8% in the delayed cohort (P = 0.040). Early necrosectomy had lower rates of mechanical ventilation (2.9% vs 10.9%, P < 0.001), septic shock (8% vs 19.5%, P < 0.001), and in-hospital mortality (1.1% vs 4.3%, P = 0.01). Patients in the early intervention group incurred lower healthcare costs, with median total charges of $52202 compared to $147418 in the delayed group. Participants in the early cohort also had a relatively shorter median length of stay (6 vs 16 days, P < 0.001). The timing of necrosectomy did not significantly influence the risk of 30-day readmission, with a hazard ratio of 0.56 (95% confidence interval: 0.31-1.02, P = 0.06). CONCLUSION: Our findings show that early necrosectomy is associated with better clinical outcomes and lower healthcare costs. Delayed intervention does not significantly alter the risk of 30-day readmission.

Predictors of Morbidity, Mortality, and Hospital Utilization Among Endoscopic Retrograde Cholangiopancreatography-related Hospitalizations: A Five-year Nationwide Assessment.

OBJECTIVE: We aimed to assess the hospital frailty risk score on the inpatient mortality, morbidity, and health care resource utilization among endoscopic retrograde cholangiopancreatography (ERCP)-related hospitalizations. BACKGROUND: Data regarding the inpatient mortality, morbidity, and health care resource utilization of ERCP among frail individuals remain limited. MATERIALS AND METHODS: Using the Nationwide Inpatient Sample, we compared the odds of inpatient mortality and morbidity of ERCP-related hospitalizations among individuals with low frailty scores, intermediate frailty scores (IFSs), and high frailty scores (HFSs). RESULTS: Overall, 776,025 ERCP-related hospitalizations were recorded from 2016 to 2020. 552,045 had a low frailty score, whereas 217,875 had an IFS, and 6105 had an HFS. Frail individuals had a 5-fold increase in mortality [IFS: adjusted odds ratio (aOR) = 4.81, 95% CI: 3.77-6.14; HFS: aOR = 4.62, 95% CI: 2.48-8.63]. An IFS was associated with a 24% increase in post-ERCP pancreatitis (aOR = 1.25, 95% CI: 1.11-1.41), a 3-fold increase in post-ERCP bleeding (aOR = 2.59, 95% CI: 1.82-3.67), and a 2-fold increase in post-ERCP duct perforation (aOR = 1.91, 95% CI: 1.38-2.64). Frail individuals experienced higher odds of in-hospital morbidity, including secondary sepsis, respiratory failure, acute kidney injury, cerebrovascular accidents, deep vein thrombosis, and pulmonary embolism. CONCLUSIONS: In summary, our study presents strong evidence in support of using the hospital frailty risk score as an index to predict mortality and morbidity during ERCP-related hospitalizations. Additional caution is warranted in the management of frail individuals undergoing ERCP.

Cardiac Arrest Mortality and Disposition Patterns in United States Emergency Departments.

Background: Despite resuscitative efforts, cardiac arrest (CA) continues to result in high mortality and poor prognosis. However, a gap remains in understanding the comparative outcomes of efforts in emergency departments (ED) over recent years. This study evaluated patients with CA during ED visits, with a particular focus on outcomes of mortality and transition of care. Methods: We conducted a retrospective cohort analysis using the National Emergency Department Sample (NEDS) database. The study population included patients aged 18 years or older who visited the ED between January 2016 and December 2020. Statistical analysis of patients and hospital characteristics included chi-squared tests for independence and multivariable logistic regression models to report the associations of factors with mortality in the ED and disposition from the ED. The primary outcome measured was mortality in the ED, and the secondary outcome included transition of care. Results: A total of 699,822,424 ED visits occurred between 2016 and 2020, with 1,414,060 (0.20%) CAs. The survival rate from CA ranged from 24.6% to 28.1%. In 2020, the rate of ED CA increased to 0.27%, with an inpatient mortality rate of 58.8%. There was no significant difference in mortality between sexes (p = 0.690). There was a trend for higher mortality in the ED among patients who were self-paid. Notably, the odds of transfer from the ED to other hospitals were significantly lower in minority groups. Conclusions: Our results showed significant disparities in ED mortality and patient disposition following cardiac arrest, highlighting the need for equitable healthcare resources and policies.

Photoreceptor outer segment reflectivity with ultrahigh resolution visible light optical coherence tomography in systemic hydroxychloroquine use.

Purpose: To evaluate outer retinal organization in normal subjects and those using hydroxychloroquine (HCQ) with ultrahigh resolution visible light optical coherence tomography (VIS-OCT). Methods: Forty eyes of 22 adult subjects were recruited from a tertiary care retina practice including controls (20 eyes, 12 subjects, mean age 40±22yrs, mean logMAR BCVA 0.19, 90% female) and subjects with a history of HCQ use (20 eyes, 10 subjects, mean age 62±17yrs, mean logMAR BCVA 0.03, 67% female). Each subject was imaged using a custom-built VIS-OCT device (axial resolution 1.3µm) and FDA-approved OCT devices. Results: Using VIS-OCT, control subjects demonstrate 5 and 6 hyperreflective bands in the foveal and parafoveal regions, respectively, between the outer nuclear layer and Bruch's membrane. These bands demonstrate intensity profiles complementary to the known histopathologic distribution of rods and cones. In comparison to controls, subjects taking HCQ demonstrate blunting of all bands, particularly bands 2-4. In all cases of suspected or known toxicity, VIS-OCT demonstrated attenuation of band 3i and in no cases was there attenuation of other bands that was more severe than band 3i, suggesting that changes in the reflectivity of Band 3i may be the earliest identifiable sign of HCQ toxicity. Conclusions: VIS-OCT of the outer retina demonstrates a unique outer retinal banding pattern corresponding to photoreceptor density profiles. There is a notable attenuation of the photoreceptor outer segment reflectivity profile associated with early HCQ toxicity. This finding may be an early, and possibly reversible, sign of HCQ toxicity, primarily impacting the cones.

Emerging sensing, imaging, and computational technologies to scale nano-to macroscale rhizosphere dynamics - Review and research perspectives.

The soil region influenced by plant roots, i.e., the rhizosphere, is one of the most complex biological habitats on Earth and significantly impacts global carbon flow and transformation. Understanding the structure and function of the rhizosphere is critically important for maintaining sustainable plant ecosystem services, designing engineered ecosystems for long-term soil carbon storage, and mitigating the effects of climate change. However, studying the biological and ecological processes and interactions in the rhizosphere requires advanced integrated technologies capable of decoding such a complex system at different scales. Here, we review how emerging approaches in sensing, imaging, and computational modeling can advance our understanding of the complex rhizosphere system. Particularly, we provide our perspectives and discuss future directions in developing in situ rhizosphere sensing technologies that could potentially correlate local-scale interactions to ecosystem scale impacts. We first review integrated multimodal imaging techniques for tracking inorganic elements and organic carbon flow at nano- to microscale in the rhizosphere, followed by a discussion on the use of synthetic soil and plant habitats that bridge laboratory-to-field studies on the rhizosphere processes. We then describe applications of genetically encoded biosensors in monitoring nutrient and chemical exchanges in the rhizosphere, and the novel nanotechnology-mediated delivery approaches for introducing biosensors into the root tissues. Next, we review the recent progress and express our vision on field-deployable sensing technologies such as planar optodes for quantifying the distribution of chemical and analyte gradients in the rhizosphere under field conditions. Moreover, we provide perspectives on the challenges of linking complex rhizosphere interactions to ecosystem sensing for detecting biological traits across scales, which arguably requires using the best-available model predictions including the model-experiment and image-based modeling approaches. Experimental platforms relevant to field conditions like SMART (Sensors at Mesoscales with Advanced Remote Telemetry) soils testbed, coupled with ecosystem sensing and predictive models, can be effective tools to explore coupled ecosystem behavior and responses to environmental perturbations. Finally, we envision that with the advent of novel high-resolution imaging capabilities at nano- to macroscale, and remote biosensing technologies, combined with advanced computational models, future studies will lead to detection and upscaling of rhizosphere processes toward ecosystem and global predictions.

Magnetic resonance-guided focused ultrasound in the treatment of refractory essential tremor: a systematic review and meta-analysis.

OBJECTIVE: Magnetic resonance-guided focused ultrasound (MRgFUS) is an emerging treatment for medication-refractory essential tremor (ET). The objective of this study was to evaluate long-term (up to 5 years) safety and efficacy of unilateral MRgFUS in the treatment of ET. METHODS: The authors performed a systematic search through 4 databases to find relevant clinical studies. Binary outcomes were analyzed and reported as odds ratios and 95% confidence intervals, while continuous outcomes were analyzed and reported as standardized mean differences (SMDs) and 95% confidence intervals. Furthermore, a univariable meta-regression was performed to evaluate the association between various covariates and the outcomes including the mean difference in the Clinical Rating Scale for Tremor (CRST) score and hand tremor scores. Sensitivity analysis was performed to address any heterogeneity. RESULTS: A total of 43 studies comprising 1818 patients with ET who underwent MRgFUS were identified. Of the 1539 patients with data on sex, 1095 (71.2%) were male. The mean follow-up duration ranged from 3 months to 8.4 years among the studies. The mean total CRST score significantly decreased at 3, 6, and 12 months post-MRgFUS (SMD -4.5, p = 0.0069; SMD -4.9, p = 0.0045; and SMD -2.95, p = 0.0039, respectively). The mean hand tremor scores significantly mitigated at 3, 6, 12, 24, and 36 months post-MRgFUS (SMD -3.99, p = 0.05; SMD -4.5, p = 0.05; SMD -1.99, p < 0.0001; SMD - 2.07, p = 0.0002; and SMD -2.1, p < 0.0001, respectively). Furthermore, the mean Quality of Life in Essential Tremor Questionnaire scores were improved at 3 months (SMD -2.8, p = 0.0025), 6 months (SMD -4.1, p = 0.04), 12 months (SMD -1.57, p = 0.0004), 2 years (SMD -1.64, p = 0.0003), and 3 years (SMD -1.14, p = 0.08). Our meta-regression findings showed that sex (p = 0.03), unlike age, handedness, symptom duration, and peak energy levels at 3 months, was associated with a significantly higher mean difference in tremor severity. CONCLUSIONS: This meta-analysis provides strong evidence supporting the efficacy and safety of unilateral MRgFUS for the treatment of ET in terms of tremor severity and quality of life with acceptable adverse events.

Technique for surgical replacement of the ascending aorta with concomitant aortic valve and hemiarch replacement: a procedural guide.

In this video tutorial case report, we show how to perform an open surgical correction of an ascending aortic aneurysm in a 74-year-old patient requiring concomitant aortic valve and hemiarch replacements, presenting with symptomatic stenosis of the aortic valve and moderate dilatation of the ascending aorta.

The Perceived Weight Stigma Scale and Weight Self-Stigma Questionnaire: Rasch analysis, confirmatory factor analysis, and network analysis among Chinese adolescents.

OBJECTIVES: The objectives of the present study were to (i) re-evaluate and expand the psychometric properties of two weight stigma instruments-the Perceived Weight Stigma Scale (PWSS) and the Weight Self-Stigma Questionnaire (WSSQ) among a large sample of adolescents using advanced psychometric methods and (ii) examine how the different types of weight stigma (i.e., PWSS and WSSQ) are associated with psychological distress. STUDY DESIGN: Cross-sectional study. METHODS: In September 2023, a cross-sectional survey utilising convenience sampling was used to recruit 9995 adolescents (mean age = 16.36 years [standard deviation = 0.78]; 57.8% males). They completed the PWSS, WSSQ, and a measure on psychological distress. The data were analysed using Rasch analysis, confirmatory factor analysis (CFA), structural equation modelling (SEM), and network analysis. RESULTS: The CFA and Rasch model results showed acceptable psychometric properties regarding factor structure, factor loading, difficulty, and infit and outfit mean squares (except Items 4 and 7 of the PWSS). There was no substantial differential item functioning for any tested items across the sex and weight categories. The CFA and SEM results showed promising validity indices with significant associations between both weight stigma scales and psychological distress (i.e., depression, anxiety, and stress). Network analysis showed inter-variable connectivity between nodes PWSS3 ("People act as if they are afraid of you") and WSSQF7 ("I feel insecure about others' opinions of me"). CONCLUSIONS: Both weight stigma scales had acceptable psychometric properties and were significantly associated with psychological distress, although each assessed different types of weight stigma. This suggests that researchers and clinicians can use these scales to reliably and validly assess weight stigmas among adolescents.

Is there a sufficient supply of clinical academics for UK medical schools? A retrospective cohort study.

OBJECTIVES: Clinical academics lead research to deliver medical advancements while also teaching in medical schools to maintain high-quality medical services. The objective of this project was to determine if there is a sufficient supply of clinical academics for UK medical schools. DESIGN: Retrospective cohort study. SETTING: Data were extracted from the UK Medical Education Database and the General Medical Council (GMC) annual National Trainee Survey between 2012 and 2022. PARTICIPANTS: 1769 registered UK doctors with academic training and a certificate of completion of training. MAIN OUTCOME MEASURE: The percentage of doctors with clinical and academic training who ended up as incumbent clinical academics at UK medical schools. RESULTS: Approximately 50% of doctors with clinical and academic training were matched as incumbent clinical academics at UK medical schools. There was a low annual rate of incumbent clinical academics leaving their post. CONCLUSION: Either clinical academic trainees do not find jobs at medical schools, or they do not want the jobs that are available. These results are indicative but not conclusive as generalisation is compromised by inconsistent disclosure of data by medical schools. We discuss variables which may contribute to the loss of clinical academics and explore the health economic case for clinical academic incentive packages to improve return on training investment.

Coupling Chiral Cuboids with Wholly Auxetic Response.

Auxetic materials have been extensively studied for their design, fabrication and mechanical properties. These material systems exhibit unique mechanical characteristics such as high impact resistance, shear strength, and energy absorption capacity. Most existing auxetic materials are two-dimensional (2D) and demonstrate half-auxetic behavior, characterized by a negative Poisson's ratio when subjected to either tensile or compressive forces. Here, we present novel three-dimensional (3D) auxetic mechanical metamaterials, termed coupling chiral cuboids, capable of achieving negative Poisson's ratio under both tension and compression. We perform experiments, theoretical analysis, and numerical simulations to validate the wholly auxetic response of the proposed coupling chiral cuboids. Parametric studies are carried out to investigate the effects of structural parameters on the elastic modulus and Poisson's ratio of the coupling chiral cuboids. The results imply that the Poisson's ratio sign-switching from negative to positive can be implemented by manipulating the thickness of Z-shaped ligaments. Finally, the potential application of the coupling chiral cuboids as inner cores for impact-resistant sandwich panels is envisioned and validated. Test results demonstrate a remarkable 49.3% enhancement in energy absorption compared to conventional solid materials.

Emerging trends and demographic disparities in anal cancer mortality across the United States census regions: An analysis of National Center for Health Statistics mortality data.

AIMS: Anal cancer, despite its rarity, is a matter of serious concern in the United States, with an uptrend in recent years and marked racial disparities in mortality rates. The aim of this work was to investigate anal cancer mortality trends and sex race disparities in the United States from 1999 to 2020. METHOD: This is a retrospective study using data from the CDC WONDER database (1999-2020). We investigated deaths attributed to anal cancer, identified by the ICD-10 code C21.1, and excluded individuals aged 14 years and under. The Mann-Kendall trend test was used to investigate temporal trends and a t-test was used to compare continuous variables. RESULTS: Both male and female age-adjusted mortality attributed to anal cancer increased significantly during the study period across all subgroups, including race (Black and White), US Census region (Northeast, Midwest, South and West) and age (15-64 and ≥65 years) (p < 0.001 for all comparisons). For each subgroup, women demonstrated significantly higher rates of mortality than men, except in the Black population, where Black men had higher rates than Black women (0.40 vs. 0.29, p < 0.001). Additionally, Black men had significantly higher mean mortality rates than White men (0.40 vs. 0.27, p < 0.001). The highest rates of anal cancer mortality were among geriatric individuals, especially women aged ≥65 years, at 1.18 per 100 000. CONCLUSION: The rise in anal cancer mortality and racial and sex disparities present a significant challenge for healthcare providers and policy makers. Further studies are required to devise evidence-based strategies to effectively tackle this challenge.

Comparison between problematic use of social media and YouTube to insomnia among Iranian adolescents: A mediating role of psychological distress.

Objective: Problematic use of the internet has been linked to emotional and sleep concerns, although relationships with specific types of internet use are less well understood. YouTube, as an online platform with video-watching features, may attract individuals to spend considerable time, for those experiencing problematic use be termed problematic use of social media (PUSM) or problematic use of YouTube (PUY). Therefore, the present study investigated relationships between PUSM/PUY, psychological distress, and insomnia among the Iranian adolescents. Methods: An online survey comprising Bergen Social Media Addiction Scale, YouTube Addiction Scale, Depression, Anxiety, Stress-21 Scale, and Insomnia Severity Index recruited 1352 participants. Results: Results of Hayes' Process Macro showed significant correlations between the two types of problematic use and insomnia, with psychological distress as a mediator (unstandardized coefficient = 0.096 and 0.100). Conclusion: The findings implied the effect of psychological distress in mediating the relationships of PUSM and PUY to insomnia.

Biological and Methodological Variability in Retinal Nerve Fiber Layer OCT: The Framingham Heart Study.

Objective: To explore participant-level biological attributes and scan-level methodological attributes associated with retinal nerve fiber layer (RNFL) thickness variability in a population-based sample of elderly United States adults. Design: Cross-sectional analysis using data from the Framingham Heart Study. Participants: One thousand three hundred forty-seven eyes from 825 participants with ≥1 OCT scan and axial length data were included. Methods: Three or more successive RNFL scans of each eye of each participant were obtained in a single session. Multivariable linear mixed models were employed to explore the associations between average RNFL thickness with participant-level biological attributes (age, gender, race, ethnicity, and axial length) and scan-level attributes (signal strength [SS]) as independent variables in the whole population as well as a subsample of adults with no self-reported history of glaucoma. Similar analyses were designed to assess methodological variability with average within-eye standard deviation (SD) for repeated scans as the dependent variable. Main Outcomes Measures: (1) Biological variability: average RNFL thickness, and (2) methodological variability: average within-participant SD across repeated scans. Results: Age (ß =  - 0.19 microns/year, [95% confidence interval {CI}: - 0.29, - 0.09]), female gender (ß = +1.48 microns vs. male, [95% CI: 0.09, 2.86]), axial length (ß =  - 1.24 microns/mm of greater length, [95% CI: - 1.80, - 0.67]), and SS (ß = +1.62 microns/1 unit greater SS, [95% CI: 1.16, 2.09]) were significantly associated with RNFL thickness, while race and ethnicity were not (P > 0.05). In analyses designed to assess methodological variability, higher RNFL thickness (ß = +0.02 per micron increase, [95% CI: 0.01, 0.03]), and lower SS (ß = +0.19 per 1 unit lower SS, [95% CI: 0.10, 0.27]) were significantly associated with greater RNFL variability. In adults with no self-reported history of glaucoma (n of eyes = 1165, n of participants = 712), female gender was not associated with RNFL, while African American race was associated with thicker RNFL (ß = +4.65 microns vs. Whites, [95% CI: 1.28, 8.03]). Conclusions: Retinal nerve fiber layer thickness is lower with older age, male gender, greater axial length, lower SS, and Whites (as compared with African Americans) without self-reported glaucoma. Measurement variability (SD) is higher with greater RNFL thickness and lower SS. Understanding these biological and methodological variations is important to aid in OCT interpretation. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Network excitability of stimulation-induced spectral responses helps localize the seizure onset zone.

OBJECTIVE: While evoked potentials elicited by single pulse electrical stimulation (SPES) may assist seizure onset zone (SOZ) localization during intracranial EEG (iEEG) monitoring, induced high frequency activity has also shown promising utility. We aimed to predict SOZ sites using induced cortico-cortical spectral responses (CCSRs) as an index of excitability within epileptogenic networks. METHODS: SPES was conducted in 27 epilepsy patients undergoing iEEG monitoring and CCSRs were quantified by significant early (10-200 ms) increases in power from 10 to 250 Hz. Using response power as CCSR network connection strengths, graph centrality measures (metrics quantifying each site's influence within the network) were used to predict whether sites were within the SOZ. RESULTS: Across patients with successful surgical outcomes, greater CCSR centrality predicted SOZ sites and SOZ sites targeted for surgical treatment with median AUCs of 0.85 and 0.91, respectively. We found that the alignment between predicted and targeted SOZ sites predicted surgical outcome with an AUC of 0.79. CONCLUSIONS: These findings indicate that network analysis of CCSRs can be used to identify increased excitability of SOZ sites and discriminate important surgical targets within the SOZ. SIGNIFICANCE: CCSRs may supplement traditional passive iEEG monitoring in seizure localization, potentially reducing the need for recording numerous seizures.

RhizoMAP: a comprehensive, nondestructive, and sensitive platform for metabolic imaging of the rhizosphere.

BACKGROUND: Elucidating the intricate structural organization and spatial gradients of biomolecular composition within the rhizosphere is critical to understanding important biogeochemical processes, which include the mechanisms of root-microbe interactions for maintaining sustainable plant ecosystem services. While various analytical methods have been developed to assess the spatial heterogeneity within the rhizosphere, a comprehensive view of the fine distribution of metabolites within the root-soil interface has remained a significant challenge. This is primarily due to the difficulty of maintaining the original spatial organization during sample preparation without compromising its molecular content. RESULTS: In this study, we present a novel approach, RhizoMAP, in which the rhizosphere molecules are imprinted on selected polymer membranes and then spatially profiled using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI). We enhanced the performance of RhizoMAP by combining the use of two thin (< 20 µm) membranes (polyester and polycarbonate) with distinct MALDI sample preparations. This optimization allowed us to gain insight into the distribution of over 500 different molecules within the rhizosphere of poplar (Populus trichocarpa) grown in rhizoboxes filled with mycorrhizae soil. These two membranes, coupled with three different sample preparation conditions, enabled us to capture the distribution of a wide variety of molecules that included phytohormones, amino acids, sugars, sugar glycosides, polycarboxylic acids components of the Krebs cycle, fatty acids, short aldehydes and ketones, terpenes, volatile organic compounds, fertilizers from the soil, and others. Their spatial distribution varies greatly, with some following root traces, others showing diffusion from roots, some associated with soil particles, and many having distinct hot spots along the plant root or surrounding soil. Moreover, we showed how RhizoMAP can be used to localize the origin of the molecules and molecular transformation during root growth. Finally, we demonstrated the power of RhizoMAP to capture molecular distributions of key metabolites throughout a 20 cm deep rhizosphere. CONCLUSIONS: RhizoMAP is a method that provides nondestructive, untargeted, broad, and sensitive metabolite imaging of root-associated molecules, exudates, and soil organic matter throughout the rhizosphere, as demonstrated in a lab-controlled native soil environment.

Validation and Measurement Invariance of the Tendency to Avoid Physical Activity and Sport Scale (TAPAS) Among Thai Young Adults.

Examining ways of reducing physical inactivity has been at the forefront of public health research. Moreover, valid and reliable scales are needed to objectively assess physical activity (PA) avoidance. Previous research has shown that experiencing weight stigma and physical appearance-related concerns are associated with physical inactivity. However, there is currently no Thai instrument that assesses physical inactivity in relation to weight stigma. Therefore, the present study examined the psychometric properties of the Thai version of the Tendency to Avoid Physical Activity and Sport Scale (TAPAS). Thai university students (N = 612) recruited via convenience sampling completed an online survey using SurveyMonkey between September 2022 and January 2023. Confirmatory factor analysis (CFA), multigroup CFA, and Pearson correlations (between TAPAS scores, age, body mass index, and time spent exercising) were used to analyze the data. The CFA showed robust psychometric properties for the Thai version of TAPAS regarding its unidimensional structure. The TAPAS was measurement invariant across sex, weight status, and daily hours of exercise. However, no significant Pearson correlations were found. In general, the results showed that the TAPAS is a good scale for assessing PA avoidance among Thai young adults across different sexes, weight status, and daily hours of exercise.

Confidence-Aware Severity Assessment of Lung Disease from Chest X-Rays Using Deep Neural Network on a Multi-Reader Dataset.

In this study, we present a method based on Monte Carlo Dropout (MCD) as Bayesian neural network (BNN) approximation for confidence-aware severity classification of lung diseases in COVID-19 patients using chest X-rays (CXRs). Trained and tested on 1208 CXRs from Hospital 1 in the USA, the model categorizes severity into four levels (i.e., normal, mild, moderate, and severe) based on lung consolidation and opacity. Severity labels, determined by the median consensus of five radiologists, serve as the reference standard. The model's performance is internally validated against evaluations from an additional radiologist and two residents that were excluded from the median. The performance of the model is further evaluated on additional internal and external datasets comprising 2200 CXRs from the same hospital and 1300 CXRs from Hospital 2 in South Korea. The model achieves an average area under the curve (AUC) of 0.94 ± 0.01 across all classes in the primary dataset, surpassing human readers in each severity class and achieves a higher Kendall correlation coefficient (KCC) of 0.80 ± 0.03. The performance of the model is consistent across varied datasets, highlighting its generalization. A key aspect of the model is its predictive uncertainty (PU), which is inversely related to the level of agreement among radiologists, particularly in mild and moderate cases. The study concludes that the model outperforms human readers in severity assessment and maintains consistent accuracy across diverse datasets. Its ability to provide confidence measures in predictions is pivotal for potential clinical use, underscoring the BNN's role in enhancing diagnostic precision in lung disease analysis through CXR.

Optimal inputs for machine learning models in predicting total joint arthroplasty outcomes: a systematic review.

INTRODUCTION: Machine learning (ML) models may offer a novel solution to reducing postoperative complication rates and improving post-surgical outcomes after total joint arthroplasty (TJA). However, the variety of different ML models that exist paired with the increasing number of potential inputs can make the implementation of this tool challenging. Therefore, we conducted a systematic review to assess the most optimal inputs of different ML models in predicting postoperative (1) medical outcomes, (2) orthopedic outcomes, and (3) patient-reported outcome measures (PROMs) after total joint arthroplasty. METHODS: The PubMed, MEDLINE, EBSCOhost, and Google Scholar databases were utilized to identify all studies evaluating ML models predicting outcomes following TJA between January 1, 2000, and June 23, 2023 (PROSPERO study protocol registration: CRD42023437586). The mean risk of bias in non-randomized studies-of interventions score, was 13.8 ± 0.5. Our initial query yielded 656 articles, of which 25 articles aligned with our aims, examining over 20 machine learning models and 1,555,300 surgeries. The area under the curve (AUC), accuracy, inputs, and the importance of each input were reported. RESULTS: Twelve studies evaluating medical complications with 13 ML models reported AUCs ranging from 0.57 to 0.997 and accuracy between 88% and 99.98%. Key predictors included age, hyper-coagulopathy, total number of diagnoses, admission month, and malnutrition. Five studies evaluating orthopedic complications with 10 ML models reported AUCs from 0.49 to 0.93 and accuracy ranging from 92 to 97%, with age, BMI, CCI, AKSS scores, and height identified as key predictors. Ten studies evaluating PROMs comprising of 12 different ML models had an AUC ranging from 0.453 to 0.97 ranked preoperative PROMs as the post-predictive input. Overall, age was the most predictive risk factor for complications post-total joint arthroplasty (TJA). CONCLUSION: These studies demonstrate the predictive capabilities of these models for anticipating complications and outcomes. Furthermore, these studies also highlight ML models' ability to identify non-classical variables not commonly considered in addition to confirming variables known to be crucial. To advance the field, forthcoming research should adhere to established guidelines for model development and training, employ industry-standard input parameters, and subject their models to external validity assessments.