Classification Of Events Research Articles

Superior predictive value of estimated pulse wave velocity for all-cause and cardiovascular disease mortality risk in U.S. general adults

BackgroundEstimated pulse wave velocity (ePWV) has been proposed as a potential approach to estimate carotid-femoral pulse wave velocity. However, the potential of ePWV in predicting all-cause mortality (ACM) and cardiovascular disease mortality (CVM) in the general population is unclear.MethodsWe conducted a prospective cohort study using the data of 33,930 adults (age ≥ 20 years) from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2014 until the end of December 2019. The study outcomes included ACM and CVM. Survey-weighted Cox proportional hazards models were used to assess hazard ratios (HRs) and 95% confidence intervals (CIs) to determine the association between ePWV and ACM and CVM. To further investigate whether ePWV was superior to traditional risk factors in predicting ACM and CVM, comparisons between ePWV and the Framingham Risk Score (FRS) and Pooled Cohort Equations (PCE) models were performed. Integrated Discriminant Improvement (IDI) and Net Reclassification Improvement (NRI) were employed to analyze differences in predictive ability between models.ResultsThe weighted mean age of the 33,930 adults included was 45.2 years, and 50.28% of all participants were men. In the fully adjusted Cox regression model, each 1 m/s increase in ePWV was associated with 50% and 49% increases in the risk of ACM (HR 1.50; 95% CI, 1.45–1.54) and CVM (HR 1.49; 95% CI, 1.41–1.57), respectively. After adjusting for FRS, each 1 m/s increase in ePWV was still associated with 29% (HR 1.29; 95% CI, 1.24–1.34) and 34% (HR 1.34; 95% CI, 1.23–1.45) increases in the risk of ACM and CVM, respectively. The area under the curve (AUC) predicted by ePWV for 10-year ACM and CVM were 0.822 and 0.835, respectively. Compared with the FRS model, the ePWV model improved the predictive value of ACM and CVM by 5.1% and 3.8%, respectively, with no further improvement in event classification. In comparison with the PCE model, the ePWV model’s ability to predict 10-year ACM and CVM was improved by 5.1% and 3.5%, and event classification improvement was improved by 34.5% and 37.4%.ConclusionsIn the U.S. adults, ePWV is an independent risk factor for ACM and CVM and is independent of traditional risk factors. In the general population aged 20 to 85 years, ePWV has a robust predictive value for the risk of ACM and CVM, superior to the FRS and PCE models. The predictive power of ePWV likely originates from the traditional risk factors incorporated into its calculation, rather than from an indirect association with measured pulse wave velocity.

Classification of posttransfusion adverse events using a publicly available artificial intelligence system.

Correct classification of transfusion reactions is important not only for effective patient care and donor management but also for accurate tracking of events in hemovigilance systems. We compared the ability of a generative artificial intelligence (AI) system to correctly diagnose hypothetical clinical situations as transfusion reactions in comparison to previous studies reporting the accuracy of transfusion medicine (TM) specialists in assessing these cases. An AI system was requested to assess 36 case scenarios to provide a diagnosis, severity, and imputability of the transfusion reactions using the CDC National Healthcare Safety Network (NHSN) criteria. Responses were compared to an expert panel's classifications and to the published responses of a panel of TM specialists. Additionally, the AI's responses were compared to the TM specialists' prior attempts to use the TrDDx web-based algorithm for the five most challenging cases. The AI's classification accuracy varied widely depending on the NHSN category. The AI accurately classified all transfusion-associated circulatory overload and transfusion-related acute lung injury cases, exceeding TM specialists' assessments. Conversely, it did not correctly identify any cases in select NHSN categories such as DSTR. Overall accuracy among all diagnostic categories was 48.7% for AI responses versus 72.1% for prior TM specialist responses (p = 0.005). AI-generated responses included non-standard terminology, limited severity assessments, and no imputability determinations. A generative AI system may have a role in helping healthcare providers to consider transfusion reaction categories that might be missed, but caution is advised in applying the AI's output to transfusion reaction classification at present.

Outcomes of High-Grade Immune Checkpoint Inhibitor Hepatitis in Hospitalized and Nonhospitalized Patients

Guidelines recommend hospitalization for severe immune checkpoint inhibitor (ICI) hepatitis. We compared patient outcomes in the inpatient versus outpatient settings. We conducted a multicenter, retrospective cohort study of 294 ICI-treated patients who developed grade 3-4 ICI hepatitis. The primary outcome was time to alanine aminotransferase (ALT) normalization (≤40); secondary outcomes included time to ALT ≤100 U/L and time to death. To account for confounding by indication, inverse probability of treatment weighting was applied to perform Cox regression. A sensitivity analysis was performed excluding patients with grade 4 hepatitis. One hundred and sixty-six patients (56.5%) were hospitalized for a median of 6 (interquartile range, 3-11) days. On inverse probability of treatment weighting Cox regression, hospitalization was not associated with time to ALT normalization (hazard ratio [HR], 1.11; 95% confidence interval [CI], 0.86-1.43; P= .436) or time to ALT ≤100 U/L (HR, 1.11; 95% CI, 0.86-1.43; P=.420). In the sensitivity analysis limited to patients with grade 3 hepatitis, hospitalization was also not associated with time to ALT normalization (HR, 1.11; 95% CI, 0.83-1.50; P= .474) or time to ALT ≤100 U/L (HR, 1.19; 95% CI, 0.90-1.58; P= .225). In a subgroup analysis of 152 patients with melanoma, hospitalization was not associated with reduced risk of all-cause death (HR, 0.93; 95% CI, 0.53-1.64; P= .798). Notably, despite their Common Terminology Criteria for Adverse Events classification of high-grade hepatitis, 94% of patients had "mild" liver injury based on International Drug-Induced Liver Injury Criteria. Hospitalization of patients with high-grade ICI hepatitis was not associated with faster hepatitis resolution and did not affect mortality. Routine hospitalization may not be necessary in all patients with high-grade ICI hepatitis and Common Terminology Criteria for Adverse Events criteria may overestimate severity of liver injury.

A novel identification method of microseismic events based on empirical mode decomposition and artificial neural network features

Microseismic technology has been widely used in geological hazard monitoring. However, the effective identification and classification of microseismic events in mines have always been a challenge in hazard monitoring. In this paper, a new method combining the empirical mode decomposition (EMD) algorithm and artificial neural network is proposed for the classification and identification of microseismic waveforms. This is the first study to explore such a model in the microseismic monitoring of coal mines. The data for this study were collected from a coal mine in northern China. A total of 2768 microseismic events and 2435 non-microseismic events were manually selected from a large amount of data. Firstly, the microseismic waveform reconstruction was performed using the 4th to 8th Intrinsic Mode Function (IMF) obtained by the EMD algorithm. Secondly, the peak factor, clearance factor, impulse factor, kurtosis, skewness, and waveform factor of each reconstructed microseismic waveform were used as features. Subsequently, the convolutional neural network (CNN) was used to classify microseismic events. Among them, data from 3600 microseismic events were used as the training set, and data from 1603 microseismic events were used as the test set. Finally, the original waveform data were used as input and compared with the classification results processed with the EMD algorithm. The classification methods of the BP neural network and Radial Basis Function (RBF) neural network were used for verification. The results of the processed features showed a significant increase in accuracy. The classification accuracy of CNN, BP neural network, and RBF neural network are 97.37%, 91.99%, and 94.23%, respectively. The results show that the utilization of the feature processing technique and CNN algorithm in this study demonstrates superior efficacy in microseismic event classification, which can be used in practice.

Safety and Diagnostic Efficacy of Image-Guided Biopsy of Small Renal Masses.

Image-guided renal mass biopsy is gaining increased diagnostic acceptance, but there are limited data concerning the safety and diagnostic yield of biopsy for small renal masses (≤4 cm). This study evaluated the safety, diagnostic yield, and management after image-guided percutaneous biopsy for small renal masses. A retrospective IRB-approved study was conducted on patients who underwent renal mass biopsy for histopathologic diagnosis at a single center from 2015 to 2021. Patients with a prior history of malignancy or a renal mass >4 cm were excluded. Descriptive statistics were used to summarize patient demographics, tumor size, the imaging modality used for biopsy, procedure details, complications, pathological diagnosis, and post-biopsy management. A biopsy was considered successful when the specimen was sufficient for diagnosis without need for a repeat biopsy. Complications were graded according to the SIR classification of adverse events. A chi-squared test (significance level set at p ≤ 0.05) was used to compare the success rate of biopsies in different lesion size groups. A total of 167 patients met the inclusion criteria. The median age was 65 years (range: 26-87) and 51% were male. The median renal mass size was 2.6 cm (range: one-four). Ultrasound was solely employed in 60% of procedures, CT in 33%, a combination of US/CT in 6%, and MRI in one case. With on-site cytopathology, the median number of specimens obtained per procedure was four (range: one-nine). The overall complication rate was 5%. Grade A complications were seen in 4% (n = 7), consisting of perinephric hematoma (n = 6) and retroperitoneal hematoma (n = 1). There was one grade B complication (0.5%; pain) and one grade D complication (0.5%; pyelonephritis). There was no patient mortality within 30 days post-biopsy. Biopsy was successful in 88% of cases. A sub-group analysis showed a success rate of 85% in tumors <3 cm and 93% in tumors ≥3 cm (p = 0.01). Pathological diagnoses included renal cell carcinoma (65%), oncocytoma (18%), clear cell papillary renal cell tumors (9%), angiomyolipoma (4%), xanthogranulomatous pyelonephritis (1%), lymphoma (1%), high-grade papillary urothelial carcinoma (1%), and metanephric adenoma (1%), revealing benign diagnosis in 30% of cases. The most common treatment was surgery (40%), followed by percutaneous cryoablation (22%). In total, 37% of patients were managed conservatively, and one patient received chemotherapy. This study demonstrates the safety and diagnostic efficacy of image-guided biopsy of small renal masses. The diagnostic yield was significantly higher for masses 3-4 cm in size compared to those <3 cm. The biopsy results showed a high percentage of benign diagnoses and informed treatment decisions in most patients.

Matrix Profile data mining for BGP anomaly detection

The Border Gateway Protocol (BGP), acting as the communication protocol that binds the Internet, remains vulnerable despite Internet security advancements. This is not surprising, as the Internet was not designed to be resilient to cyber-attacks, therefore the detection of anomalous activity was not of prime importance to the Internet creators. Detection of BGP anomalies can potentially provide network operators with an early warning system to focus on protecting networks, systems, and infrastructure from significant impact, improve security posture and resilience, while ultimately contributing to a secure global Internet environment. In this paper, we present a novel technique for the detection of BGP anomalies in different events. This research uses publicly available datasets of BGP messages collected from the repositories, Route Views and Réseaux IP Européens (RIPE). Our contribution is the application of a time series data mining approach, Matrix Profile (MP), to detect BGP anomalies in all categories of BGP events. Advantages of the MP detection technique compared to extant approaches include that it is domain agnostic, is assumption-free, requires few parameters, does not require training data, and is scalable and storage efficient. The single hyper-parameter analyzed in MP shows it is robust to change. Our results indicate the MP detection scheme is competitive against existing detection schemes. A novel BGP anomaly detection scheme is also proposed for further research and validation.

KEPA-CRF: Knowledge expansion prototypical amortized conditional random field for few-shot event detection

Event Detection (ED) has long struggled with the ambiguous definition of event categories, making it challenging to accurately classify events. Previous endeavors aimed to tackle this problem by constructing prototypes for specific event categories. However, they overlooked potential correlations among instances of distinct event categories, resulting in trigger misclassifications across event types. In this research, we introduce KEPA-CRF to train enhanced event prototypes and address the issue of limited samples in few-shot event detection. By integrating external knowledge from the Glove knowledge base into the model training process, we augment synonymous examples, mitigating the problem of insufficient samples in few-shot event detection. Additionally, through prototype adversarial training, we contrast prototypes of different event categories to augment the representational capabilities of prototype vectors. Experimental results showcase that our approach attains superior performance on the benchmark dataset FewEvent, surpassing comparative models with reduced training time.

Cycle mapping with adversarial event classification network for fake news detection

Cycle mapping with adversarial event classification network for fake news detection

Adverse Events Associated With Disease-Modifying Drugs for Multiple Sclerosis: A Multiregional Population-Based Study

It is not possible to fully establish the safety of a disease-modifying drug (DMD) for multiple sclerosis (MS) from randomized controlled trials as only very common adverse events occurring over the short-term can be captured, and the quality of reporting has been variable. We examined the relationship between the DMDs for MS and potential adverse events in a multiregion population-based study. We identified people with MS using linked administrative health data from 4 Canadian provinces. MS cases were followed from the most recent of first MS or related demyelinating disease event on January 1, 1996, until the earliest of emigration, death, or December 31, 2017. DMD exposure primarily comprised β-interferon, glatiramer acetate, natalizumab, fingolimod, dimethyl fumarate, teriflunomide, and alemtuzumab. We examined associations between DMD exposure and infection-related hospitalizations and physician visits using recurrent events proportional means models and between DMD exposure and 15 broad categories of incident adverse events using stratified multivariate Cox proportional hazard models. We identified 35,894 people with MS. While virtually all DMDs were associated with a 42%-61% lower risk of infection-related hospitalizations, there was a modest increase in infection-related physician visits by 10%-33% for select DMDs. For incident adverse events, most elevated risks involved a second-generation DMD, with alemtuzumab's hazard of thyroid disorders being 19.42 (95% CI 9.29-36.51), hypertension 4.96 (95% CI 1.78-13.84), and cardiovascular disease 3.72 (95% CI 2.12-6.53). Natalizumab's highest risk was for cardiovascular disease (adjusted hazard ratio [aHR] 1.61; 95% CI 1.24-2.10). For the oral DMDs, fingolimod was associated with higher hazards of cerebrovascular (aHR 2.04; 95% CI 1.27-3.30) and ischemic heart diseases (aHR 1.64; 95% CI 1.10-2.44) and hypertension (aHR 1.73; 95% CI 1.30-2.31); teriflunomide with higher hazards of thyroid disorders (aHR 2.30; 95% CI 1.11-4.74), chronic liver disease (aHR 1.94; 95% CI 1.19-3.18), hypertension (aHR 1.76; 95% CI 1.32-2.37), and hyperlipidemia (aHR 1.61; 95% CI 1.07-2.44); and from complementary analyses (in 1 province), dimethyl fumarate with acute liver injury (aHR 6.55; 95% CI 1.96-21.87). Our study provides an extensive safety profile of several different DMDs used to treat MS in the real-world setting. Our findings not only complement those observed in short-term clinical trials but also provide new insights that help inform the risk-benefit profile of the DMDs used to treat MS in clinical practice. The results of this study highlight the continued need for long-term, independent safety studies of the DMDs used to treat MS. This study provides Class III evidence that for patients with MS, while DMD exposure reduces the risk of infection-related hospitalizations, there are increased risks of infection-related physician visits and incident adverse events for select DMDs.

Long-Term Spatiotemporal Investigation of Various Rainfall Intensities over Central India Using EO Datasets

This study involved an investigation of the long-term seasonal rainfall patterns in central India at the district level during the period from 1991 to 2020, including various aspects such as the spatiotemporal seasonal trend of rainfall patterns, rainfall variability, trends of rainy days with different intensities, decadal percentage deviation in long-term rainfall patterns, and decadal percentage deviation in rainfall events along with their respective intensities. The central region of India was meticulously divided into distinct subparts, namely, Gujarat, Daman and Diu, Maharashtra, Goa, Dadra and Nagar Haveli, Madhya Pradesh, Chhattisgarh, and Odisha. The experimental outcomes represented the disparities in rainfall distribution across different districts of central India with the spatial distribution of mean rainfall ranges during winter (2.08 mm over Dadra and Nagar Haveli with an average of 24.19 mm over Odisha), premonsoon (6.65 mm over Gujarat to 132.89 mm over Odisha), monsoon (845.46 mm over Gujarat to 3188.21 mm over Goa), and post-monsoon (30.35 mm over Gujarat to 213.87 mm over Goa), respectively. Almost all the districts of central India displayed an uneven pattern in the percentage deviation of seasonal rainfall in all three decades for all seasons, which indicates the seasonal rainfall variability over the last 30 years. A noticeable variation in the percentage deviation of seasonal rainfall patterns has been observed in the following districts: Rewa, Puri, Anuppur, Ahmadabad, Navsari, Chhindwara, Devbhumi Dwarka, Amreli, Panch Mahals, Kolhapur, Kandhamal, Ratnagiri, Porbandar, Bametara, and Sabar Kantha. In addition, a larger number of rainy days of various categories occurred in the monsoon season in comparison to other seasons. A higher contribution of trace rainfall events was found in the winter season. The highest contributions of very light, light rainfall, moderate, rather high, and high events were found in the monsoon season in central India. The percentage of various categories of rainfall events has decreased over the last two decades (2001–2020) in comparison to the third decade (1991–2000), according to the mean number of rainfall events in the last 30 years. This spatiotemporal analysis provides valuable insights into the rainfall trends in central India, which represent regional disparities and the potential challenges impacted by climate patterns. This study contributes to our understanding of the changing rainfall dynamics and offers crucial information for effective water resource management in the region.

Monitoring the Workflow of Earthquake by Using Machine Learning

This study explores the innovative application of machine learning techniques in monitoring and analyzing the workflow associated with earthquakes. The primary objective is to enhance the accuracy and efficiency of earthquake detection, prediction, and response mechanisms the development of machine learning technology enables more robust real-time earthquake monitoring through automated implementations. However, the application of machine learning to earthquake location problems faces challenges in regions with limited available training data. This paper proposes a machine learning-based approach to monitor seismic workflows efficiently. We apply various Machine Learning algorithms, including deep learning neural networks and decision trees, to process and interpret vast amounts of seismic data. These algorithms were trained on historical earthquake records to identify patterns and anomalies that precede seismic events. Our approach not only focused on the detection of seismic activities but also on the prediction of potential aftershock locations and magnitudes. Earthquake monitoring and response systems play a crucial role in mitigating potential damages and ensuring rapid response to seismic events. Traditional methods rely on sensor networks and manual analysis, often leading to delays in detection and response. The system leverages real-time data from seismic sensors, incorporating advanced machine learning algorithms for data analysis and pattern recognition. By employing deep learning models, such as convolution neural networks (CNNs) and recurrent neural networks (RNNs), the system can detect and classify seismic activities accurately. Additionally, anomaly detection techniques enable the identification of unusual seismic patterns that might indicate impending earthquakes or aftershocks. The system integrates geographical information systems (GIS) to map the seismic activities spatially, aiding in the visualization and understanding of seismic patterns across regions. The machine learning models are trained on historical seismic data to enhance their predictive capabilities and adaptability to varying seismic behaviors. This proposed system offers a real-time monitoring solution that can assist in early earthquake detection, accurate event classification, and timely response coordination. Its ability to continuously learn from incoming data ensures adaptability to changing seismic behaviors, thereby improving overall efficiency and reliability in earthquake monitoring workflows.

Exploring the Landscape of Immune Checkpoint Inhibitor-Induced Adverse Events Through Big Data Mining of Pan-Cancer Clinical Trials.

Immune checkpoint inhibitors (ICIs) have significantly improved the survival of patients with cancer and provided long-term durable benefit. However, ICI-treated patients develop a range of toxicities known as immune-related adverse events (irAEs), which could compromise clinical benefits from these treatments. As the incidence and spectrum of irAEs differs across cancer types and ICI agents, it is imperative to characterize the incidence and spectrum of irAEs in a pan-cancer cohort to aid clinical management. We queried >400 000 trials registered at ClinicalTrials.gov and retrieved a comprehensive pan-cancer database of 71 087 ICI-treated participants from 19 cancer types and 7 ICI agents. We performed data harmonization and cleaning of these trial results into 293 harmonized adverse event categories using Medical Dictionary for Regulatory Activities. We developed irAExplorer (https://irae.tanlab.org), an interactive database that focuses on adverse events in patients administered with ICIs from big data mining. irAExplorer encompasses 71 087 distinct clinical trial participants from 343 clinical trials across 19 cancer types with well-annotated ICI treatment regimens and harmonized adverse event categories. We demonstrated a few of the irAE analyses through irAExplorer and highlighted some associations between treatment- or cancer-specific irAEs. The irAExplorer is a user-friendly resource that offers exploration, validation, and discovery of treatment- or cancer-specific irAEs across pan-cancer cohorts. We envision that irAExplorer can serve as a valuable resource to cross-validate users' internal datasets to increase the robustness of their findings.

Safety and Effectiveness of an Enhanced Recovery Protocol in Patients Undergoing Burr Hole Evacuation for Chronic Subdural Hematoma.

Enhanced recovery programs may be especially useful in patients with chronic subdural hematoma or hygroma (cSDH), who frequently exhibit frailty and multimorbidity. We aim to evaluate the real-world safety and effectiveness of an enhanced recovery protocol in this population. From a prospective registry, burr hole evacuations for cSDH carried out under the protocol (including early thromboprophylaxis, no flat bed rest, early mobilization without drain clamping, and early resumption of antithrombotic medication) were extracted, along with those procedures carried out within the past year before protocol change. Propensity score-based matching was carried out. A range of clinical and imaging outcomes were analyzed, including modified Rankin Scale as effectiveness and Clavien-Dindo adverse event grading as safety primary end points. Per group, 91 procedures were analyzed. At discharge, there was no significant difference in the modified Rankin Scale among the standard and enhanced recovery groups (1 [1; 2] vs 1 [1; 3], P = .552), or in Clavien-Dindo adverse event grading classifications of adverse events (P = .282) or occurrence of any adverse events (15.4% vs 20.9%, P = .442). There were no significant differences in time to drain removal (2.00 [2.00; 2.00] vs 2.00 [1.25; 2.00] days, P = .058), time from procedure to discharge (4.0 [3.0; 6.0] vs 4.0 [3.0; 6.0] days, P = .201), or total hospital length of stay (6.0 [5.0; 9.0] vs 5.0 [4.0; 8.0] days, P = .113). All-cause mortality was similar in both groups (8.8% vs 4.4%, P = .289), as was discharge disposition (P = .192). Other clinical and imaging outcomes were similar too (all P > .05). In a matched cohort study comparing perioperative standard of care with a novel enhanced recovery protocol focusing on evidence-based drainage, mobilization, and thromboprophylaxis regimens as well as changes to the standardized reuptake of oral anticoagulants and antiaggregants, no differences in safety or effectiveness were observed after burr hole evacuation of cSDH.

Solar neutrino background in high-pressure gaseous 82SeF6 TPC neutrinoless double beta decay experiments* *Supported in part by the National Natural Science Foundation of China (12105110) and National Key Research and Development Program of China (2022YFA1604703)

In this study, the possibility of observing a solar neutrino background in a future neutrinoless double beta decay experiment using a high-pressure gaseous 82SeF6 TPC is investigated. Various contributions are simulated, and possible features that could be used for event classification are discussed; two types of backgrounds are identified. The rate of multi-site background events is approximately 0.63 events/(ton·yr) in a 30 keV ROI window. This background could be effectively reduced to less than 0.0001 events/(ton·yr) (95% C.L.) while maintaining a high signal efficiency of 93% by applying a selection based on the number of clusters and energy of the leading cluster. The rate of the single-electron background events is approximately 0.01 events/(ton·yr) in the ROI. Assuming a reduction factor of 10 for the single-electron background events obtained via the algorithms developed for radioactive background rejection, the total background induced by the solar neutrino would be 0.001 events/(ton·yr), which is sufficiently small for conducting ton-level experiments.

Exposure to Traumatic Events at Work, Post-Traumatic Symptoms, and Professional Quality of Life among Italian Midwives: A Cross-Sectional Study.

The aim of this study is to investigate the potential occurrence of post-traumatic stress disorder (PTSD) symptoms, following exposure to traumatic events, in Italian midwives and their consequent influence on the quality of midwives' professional lives. In addition, data were collected on the major traumatic events described by midwives. A cross-sectional study related the socio-demographic characteristics of 286 midwives with the scores obtained on two assessment scales, one for post-traumatic stress disorder (IES-R) and the other for quality of life (ProQOL V). The percentage of midwives who obtained a score higher than the predetermined threshold value in both questionnaires was noted, and the correlations that emerged were highlighted. Through this qualitative method, their significant work-related traumatic events were investigated to finally detect the prevalence percentage of each category. The proportion of midwives scoring higher than 33 on the IES-R scale, indicating a higher likelihood of PTSD, was 48.6%. Freelancers or outpatient clinic midwives had lower mean IES scores (p = 0.049). A significant inverse correlation was observed between age and IES-R score and between the compassion satisfaction subscale and time since completing education (p = 0.028). A comparison between the IES-R and ProQOL scales showed a statistically significant correlation (p < 0.001), in particular, between the burnout (BO) (p < 0.001) and secondary traumatic stress (STS) (p < 0.001) subscales. The thematic categorization of traumatic events included mother/child death, mother/child medical complications, relational problems with patients or team members, and organizational problems/medical staff's inexperience. The emerging data may confirm the data in the literature, namely those showing that midwives are prone to developing work-related PTSD, particularly due to their exposure to traumatic events such as maternal and neonatal death.

(215) Penuma® Penile Implant Adverse Events in the Manufacturer and User Facility Device Experience (MAUDE) Database

Abstract Introduction Penuma® is an FDA-approved penile implant intended to increase flaccid penile length and girth for improved cosmesis. It is the only FDA-approved product of its kind, with reportedly thousands of implants since 2004. There is a paucity of data on the adverse events related to this procedure. Objective To describe and characterize adverse events related to Penuma® using a national database. Methods The Manufacturer and User Facility Device Experience (MAUDE) database, a voluntary-reported database of medical device complications with data since 1991, was queried. The MAUDE adverse event classification system, a validated classification system, was used as a standardized tool to classify adverse events based on severity. All adverse events related to Penuma® were identified and quantified. Results 42 reports of adverse events related to Penuma® were identified (Figure 1). Of these, 35 patients were reported to undergo device removal most commonly due to infection (n=8) or protrusion (n=8). (Table 1). The most commonly reported adverse event was deformity/disfigurement (n=27), of which 18 cases reported loss of penile length. Other adverse events included: pain (n=16), infection (n=12), decreased sensitivity (n=10), scar tissue (n=9), erectile dysfunction (n=8), seroma (n=7), and erosion (n=6). Conclusions To our knowledge, this is the first study to systematically characterize publicly reported adverse events occurring with Penuma® implantation, using the MAUDE database. Further research is warranted to delineate the incidence of adverse events related to Penuma®. Disclosure No.

Classification of traumatic life events and substance use among persons admitted to inpatient psychiatry in Ontario, Canada

BackgroundTrauma is commonly overlooked or undiagnosed in clinical care settings. Undetected trauma has been associated with elevated substance use highlighting the need to prioritize identifying individuals with undetected trauma through common characteristics. ObjectiveThe purpose of this study is to identify classifications of traumatic life experiences and substance use among persons admitted to inpatient psychiatry in Ontario and to identify covariates associated with classification membership. Study designA population-based retrospective cohort study was conducted using interRAI Mental Health (MH) assessment data. Individuals were included who experienced traumatic life events (N = 10,125), in Ontario, Canada between January 1, 2015, to December 31, 2019. ResultsEight latent classes were identified that ranged from low (i.e., Class 1: Interpersonal Issues, Without Substance use) to high (i.e., Class 8: Widespread Trauma, Alcohol & Cannabis Addiction) complexity patterns of traumatic life events and substance use indicators. Classifications with similar trauma profiles were differentiated by patterns of substance use. For example, individuals in Class 2: Safety & Relationship Issues, Without Substance use and Class 3: Safety & Relationship Issues, Alcohol & Cannabis both had many estimates centered around the experience of victimization (e.g., victim of sexual assault, victim of physical assault, victim of emotional abuse). Multinomial logistic regression models highlighted additional factors associated with classifications such as homelessness, where those who were homeless were 2.09–4.02 times more likely to be in Class 6: Widespread Trauma & Substance Addiction. InterpretationTrauma exposures are complex and varied among persons in inpatient psychiatry and can be further differentiated by substance use patterns. These findings provide a population-based estimate of the trauma experiences of persons in inpatient settings in Ontario, Canada. Findings demonstrate the importance of using comprehensive assessment to support clinical decision making in relation to trauma and substance.

Characteristics of Foreshock Subsolar Compressive Structures

AbstractThe turbulent foreshock region upstream of the quasi‐parallel bow shock is dominated by waves and reflected particles that interact with each other and create a large number of different foreshock transients. The structures with the enhanced magnetic field, Short Large Amplitude Magnetic Structures, and density spikes named plasmoids are frequently observed. They are one of the suggested sources of transient flux enhancements or jets in the magnetosheath. Using measurements of the Magnetospheric Multiscale Spacecraft (MMS) and OMNI solar wind database between the 2015 and 2018 years, we have found that there is a category of events exhibiting both magnetic field and density enhancements simultaneously and we introduce the term “mixed structures” for them. Consequently, we divided our set of observations into three groups of events and present their comparative statistical analysis in the subsolar foreshock. Based on our results and previous research, we discuss the properties, possible origin and occurrence rates of these events under different upstream conditions and their possible relation to the jet and plasmoid formation in the magnetosheath.

Evaluating events data for cultural analytics: a case study on the economic and social effects of Covid-19 on the Edinburgh Festivals

The effects of the Covid-19 pandemic on the Creative and Cultural Industries can be difficult to quantify. Metadata about events (theatre productions, music and comedy gigs, sporting fixtures, days out, and more) are an untapped resource for cultural analytics that can be used as a proxy metric for financial and social impact. This article uses a sample of large-scale cultural events data from UK industry providers Data Thistle to ask: how can events data at scale be used to quantify the financial and social effects of the Covid-19 pandemic on the cultural events sector in a particular region? We analysed the changes in event provision in Edinburgh in August 2018, 2019, 2020 and 2021, revealing an estimated 97.3% fall in ticketing revenue between 2019 and 2020. Additionally, the effects that pandemic restrictions had on different categories of event reveal a disparity in how different audience sectors were affected, with ‘Visual Art’ and ‘Days Out’ showing most resilience and ‘Theatre’, ‘Comedy’ and ‘LGBT’ events being most reduced. Our findings indicate that events data are a rich but heterogenous source of information regarding the cultural and creative economy, which is not yet routinely used by researchers.

Monitoring the workflow of Earthquake by using Machine Learning

The main goal is to improve the accuracy and efficiency of earthquake detection, prediction and response through automation and, over time, to improve machine learning techniques for earthquake monitoring. However, the application of machine learning to earthquake location problems faces challenges in areas with limited data. This article presents an engine-based approach to monitor earthquake performance. We use a variety of Machine Learning algorithms, including deep learning neural networks and decision trees, for processing and interpret large amounts of seismic data. These algorithms are trained on historical earthquake records to identify patterns and anomalies prior to seismic events. Our approach focuses not only on detecting seismic events, but also on predicting the location and magnitude of earthquakes. Earthquake monitoring and response systems play an important role in reducing potential losses and providing rapid response to seismic events. Conventional methods rely on sensor networks and manual analysis, often with delays in detection and response. The system receives real-time data from seismic sensors, incorporating advanced machine learning algorithms for data analysis and pattern recognition. The system can detect and describe seismic events using deep learning models such as convolutional neural networks (CNN) and random neural networks (RNN).In addition, anomaly detection techniques allow the identification of unusual seismic patterns that may indicate impending earthquakes or earthquakes. The system integrates geographic information systems (GIS) to comprehensively map seismic activity, helping to visualize and understand seismic patterns in the region. Machine learning models are trained on historical seismic data to improve their predictive capabilities and adapt to different seismic behaviors. This proposed system provides a real-time monitoring solution that can assist in early earthquake detection, accurate event classification, and timely response coordination. The ability to continuously learn from incoming data enables adaptation to change in seismic activity, thereby increasing the efficiency and reliability of earthquake monitoring operations.