Types Of Events Research Articles

Differences in Overuse Injuries in Gender-Comparable Sports: A Nationally Representative Sample of High School Athletes.

Participation in high school sports has physical, physiological, and social development benefits, while also increasing the risk of acute and overuse injuries. Risk of sport-related overuse injury differs between boys and girls. To investigate differences in overuse injuries among US high school athletes participating in the gender-comparable sports of soccer, basketball, and baseball/softball. Descriptive epidemiology study using a nationally representative sample from the High School Reporting Information Online (RIO) database. High schools. Athletes with overuse injuries during the 2006-2007 through 2018-2019 academic years. National estimates and rates of overuse injuries were extrapolated from weighted observed numbers with the following independent variables: sport, gender, academic year, class year, event type, body site, diagnosis, recurrence, activity, and position. Among an estimated 908 295 overuse injuries nationally, 43.9% (n = 398 419) occurred in boys' soccer, basketball, and baseball, whereas 56.1% (n = 509 876) occurred in girls' soccer, basketball, and softball. When comparing gender across sports, girls were more likely to sustain an overuse injury than boys (soccer, injury rate ratio [IRR]: 1.37, 95% CI = 1.20-1.57; basketball, IRR: 1.82, 95% CI = 1.56-2.14; baseball/softball, IRR: 1.21, 95% CI = 1.04-1.41). Most overuse injuries in soccer and basketball for both genders occurred to a lower extremity (soccer: 83.9% [175 369/209 071] for boys, 90.0% [243 879/271 092] for girls; basketball: 77.0% [59 239/76 884] for boys, 80.5% [81 826/101 709] for girls), whereas most overuse injuries in baseball and softball were to an upper extremity (72.5% [81 363/112 213] for boys, 53.7% [73 557/136 990] for girls). For boys' baseball, pitching (43.5% [47 007/107 984]) was the most common activity associated with an overuse injury, which differed from the most common activity of throwing (31.7% [39 921/126 104]) for girls' softball. Gender differences observed in this study can help guide future strategies that are more specific to gender and sport to reduce overuse injuries among high school athletes.

Analysis of Reported Adverse Events Related to Hemospray: An FDA MAUDE Database Study.

Topical hemostatic powder is a mineral powder that forms an adherent barrier and coagulates active bleeding in the gastrointestinal (GI) tract. Hemospray is the first hemostatic powder approved by the Food and Drug Administration (FDA) in the United States. Hemospray has been increasingly used to manage GI bleeding. However, data on the adverse events of hemostatic powders are lacking. Therefore, we aim to report and analyze adverse events associated with Hemospray using the FDA's "Manufacturer and User Facility Device Experience" database. We analyzed the postmarketing surveillance data from the FDA's Manufacturer and User Facility Device Experience database for Hemospray, initially known as TC-325, from June 2018 through April 2022. Results of the search were classified into device-related technical issues, patient-related adverse events and health care staff-related adverse events. Five hundred two medical device reporting claims were identified from June 2018 through April 2022. Seven duplicate claims were identified, and some claims included more than one event type. Therefore, there were 558 device-related problems, 28 patient-related adverse events, and 2 adverse events in health care staff members. The most common device-related problems were activation failure or failure to fire (n = 385, 70.0%) and obstruction of carbon dioxide flow (n = 121, 21.7). The most common patient-related adverse events included tissue injury or bleeding (n = 21) and perforation (n = 5). Although Hemospray is a valuable tool in the armamentarium for endoscopists in managing GI bleeding, endoscopists must be mindful of deice-related problems and potential patient-related adverse events.

Collaborative Synthesis of Patient Records through Multi-Visit Health State Inference

Electronic health records (EHRs) have become the foundation of machine learning applications in healthcare, while the utility of real patient records is often limited by privacy and security concerns. Synthetic EHR generation provides an additional perspective to compensate for this limitation. Most existing methods synthesize new records based on real EHR data, without consideration of different types of events in EHR data, which cannot control the event combinations in line with medical common sense. In this paper, we propose MSIC, a Multi-visit health Status Inference model for Collaborative EHR synthesis to address these limitations. First, we formulate the synthetic EHR generation process as a probabilistic graphical model and tightly connect different types of events by modeling the latent health states. Then, we derive a health state inference method tailored for the multi-visit scenario to effectively utilize previous records to synthesize current and future records. Furthermore, we propose to generate medical reports to add textual descriptions for each medical event, providing broader applications for synthesized EHR data. For generating different paragraphs in each visit, we incorporate a multi-generator deliberation framework to collaborate the message passing of multiple generators and employ a two-phase decoding strategy to generate high-quality reports. Our extensive experiments on the widely used benchmarks, MIMIC-III and MIMIC-IV, demonstrate that MSIC advances state-of-the-art results on the quality of synthetic data while maintaining low privacy risks.

Abstract 4849: Mosaic chromosomal alterations in a prospective cohort of Mexican Americans for cancer risk and genetic insights

Abstract Introduction: In this study, we aim to explore mosaic chromosomal alterations (mCA) and their implications in cancer risk among Mexican Americans (MA) - an expanding yet historically underrepresented ethnic minority in the U.S. In addition, we propose to develop a novel method that identifies high mutant cell fraction (MCF) copy-neutral Loss of heterozygosity (cn-LOH). At sufficiently high MCF, these will look like runs of homozygosity (ROH) and may be missed by current mCA detection techniques that rely on identified germline heterozygote genotypes. Our preliminary findings revealed frequent and widespread homozygosity across the genome, predominantly in short regions (<10 Mb), although some exceeded 100 Mb. Our objective is to advance mCA detection techniques, differentiating cn-LOH from ROH, to deepen our understanding of mCA. Methods: We analyzed SNP array data from blood and saliva samples of 1657 individuals, focusing on cancer and chronic disease risk ages, from the Mano a Mano Mexican American Cohort Study. We applied hapLOH and MoChA, both haplotype-based methods, to detect mCA. To identify high-MCF cn-LOH, we first scanned for observed ROH using PLINK. Separately, we develop a statistical framework through simulations to differentiate high-MCF cn-LOH from ROH. We considered multiple methods; initially, we built a statistical framework using a hypothesis-testing approach (H0: ROH; H1: cn-LOH), where we compared the “B allele” frequency information within observed ROH. To split this information and obtain our groups, we used population genetic principles. Secondly, we classified our events, calculating the likelihood ratio of our statistical hypotheses. Finally, we explored a haplotype-based imputation approach that leverages flanking diploid region information. Results: We identified numerous mCA, predominantly in individuals without cancer. Simulations showed effective classification in larger homozygosity regions (>10 Mb), guiding our methodological focus on detecting shorter high-MCF regions. Not surprisingly, extremely high MCFs (0.99-1) presented challenges in accurate identification. Conclusion and Future Directions: To improve our mCA results description, we will thoroughly characterize and study their association with cancer phenotypes and environmental exposures. We are in the process of genotyping the entire cohort, which consists of over 25,000 participants. To date, our cohort includes 909 cancer cases, of which 262 are hematological malignancies. We sought to design a unique method that differentiates high-MCF cn-LOH from observed ROH, improving current mCA discovery. Our focus is on small regions as they provide limited information to infer event types. Our next step is to characterize and validate our results, especially in pathologically normal samples, as a way of identifying potential biomarkers of interest. Citation Format: Jessica Valdebenito, Justin Wong, Dahwei Chang, Henry Gomez, Kevin T Nead, Yasminka Jakubek, Paul Scheet. Mosaic chromosomal alterations in a prospective cohort of Mexican Americans for cancer risk and genetic insights [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4849.

The stance and factors of international organizations towards countries from a Chinese perspective

ABSTRACT The degree and scope of constraints imposed by International Organizations (IOs) on States are increasing, and identifying the factors affecting the IOs' stances on States is helpful to enhance the state's discourse power and influence in the international community. First, by coding the records of Regular Press Conferences of the Speaking Office of the Chinese Ministry of Foreign Affairs during the period of 2018–2022, we obtained a dataset of IOs' stances on China-related events. Second, we constructed political relation, economic relation, and humanistic relation indicators to complement the influence factors, adopted the Bayesian logit model, and applied the Monte Carlo Markov chain algorithm and Gibbs sampling to analyze the probability of IOs' positive stances towards China. The result shows that IOs' category, length of establishment, functional position, and relationship with China are all related to their tendency of making a statement about China. In terms of the heterogeneity of event types, forum-type IOs are significantly inclined to give positive assessment compared to service-type IOs on events focusing on China's own development. Further analysis reveals that the model for analyzing and predicting the attitudes of IOs is more effective when the international situation is in a stable period.

Correlation between postoperative treatment selection and prognosis determined using the Oncotype DX® test data: a retrospective multicenter study in Japan.

Oncotype DX® is a frequently used multigene assay for hormone receptor-positive breast cancers. However, limited evidence is available regarding its application in Japan owing to the lack of insurance coverage. Therefore, we conducted this large-scale, retrospective study by collecting data from nine Japanese institutes and assessed postoperative treatment choice and prognosis by using Oncotype DX®. Six hundred thirty-two patients who underwent breast surgery and whose recurrence score (RS) data were available were included. They were divided into RS 0-25 and RS ≥ 26 groups. The groups were compared in terms of clinicopathological factors, treatment options, and prognosis. After the median follow-up period of 10.1years, the disease-free survival (DFS) rates were significantly better in the RS 0-25 group (p = 0.02). Per the recurrent event type, there was no significant intergroup difference in locoregional recurrence (p = 0.139). However, a trend toward better distant DFS was observed in the RS 0-25 group (p = 0.08). Overall survival was also significantly better in this group (p = 0.027). Considering chemotherapy use, DFS worsened among chemotherapy-treated patients with an RS of 0-25 and those with an RS ≥ 26 who did not receive chemotherapy (p < 0.001). Seven (1.35%) chemotherapy-treated patients with an RS of 0-25 showed disease recurrence. This study presents the largest database-derived prognostic data in Japanese patients, utilizing the Oncotype DX® treatment selection. Further studies are needed to determine the impact on treatment choice, considering the clinical risk, and the need for additional postoperative treatment.

Multidimensional typologies of faulty medical device events: analysis, risk characterisation, and stakeholder mapping

ABSTRACT This study uses the publicly available International Medical Devices Database in three-phase research to present empirically grounded multi-dimensional typologies of events involving faulty medical devices. In the first phase, considering three major categorical data, i.e. event actions, event types, and countries, and, by employing descriptive analysis, we identify that perhaps ‘Field safety notice’ is the most dominant event type under which 66 event actions exist. In the second phase, employing the two-step clustering analysis technique, we identify that four distinct clusters exist. Finally, in the last phase, employing the focus group-driven content analysis approach using event reason description in conjunction with the SHELL (software-hardware-equipment-liveware) model, we classify the top 10 event actions. We also identify corresponding risk levels considering the top 10 sources of device problems. Further, the focus group employing the interest-influence framework in conjunction with pertinent self-addressed questions ascertained that the greatest number of top 10 event actions lies at the quadrant wherein the major stakeholder has a high degree of interest and influence in mitigating those medical events. Our study bridges the extant research gap about the lack of comprehensive, data-driven, and evidence-based themes around medical device events, the associated risk characteristics, and stakeholder mapping.

What Local Environments Drive Opportunities for Social Events? A New Approach Based on Bayesian Modeling in Dallas, Texas, USA

In-person social events bring people to places, while people and places influence where and what social events occur. Knowing what people do and where they build social relationships gives insights into the distribution and availability of places for social functions. We developed a Bayesian Network model, integrating points of interest (POIs) and sociodemographic characteristics, to estimate the probabilistic effects of places and people on the presence of social events. A case study in Dallas demonstrated the utility and performance of the model. The Bayesian Network model predicted the presence likelihoods for seven types of social events with an R2 value around 0.83 (95% confidence interval). For both the presence and absence of social events at locations, the model predictions were within a 20% error for most event types. Furthermore, the model suggested POI, age, education, and population density configurations as important contextual variables for place–event associations across locations. A spatial cluster analysis identified likely multifunctional hotspots for social events (i.e., socially vibrant places). While psychological and cultural factors likely contribute further to local likelihoods of social event occurrences, the proposed conceptually informed geospatial data-science approach elucidated intricate place–people–event relationships and implicates inclusive, participatory places for urban development.

Joint AFT random-effect modeling approach for clustered competing-risks data

Competing risks data arise when occurrence of an event hinders observation of other types of events, and they are encountered in various research areas including biomedical research. These data have been usually analyzed using the hazard-based models, not survival times themselves. In this paper, we propose a joint accelerated failure time (AFT) modeling approach to model clustered competing risks data. Times to competing events are assumed to be log-linear with normal errors and correlated through a scaled random effect that follows a zero-mean normal distribution. Inference on the model parameters is based on the h-likelihood. Performance of the proposed method is evaluated through extensive simulation studies. The simulation results show that the estimated regression parameters are robust against the violation of the assumed parametric distributions. The proposed method is illustrated with three real competing risks data sets.

UAS and a virtual environment as possible response tools 1 to the incidents 2 involving uncontrolled release of dangerous gases – a case study

Various types of events and emergency situations have a significant impact on the safety of people and the environment. This especially refers to the incidents involving the emission of pollutants, such as ammonia, into the atmosphere. The article presents the concept of combining unmanned aerial vehicles with contamination plume modelling. Such a solution allows for mapping negative effects of ammonia release caused by the damage to a tank (with set parameters) during its transport as well as by the point leakage (such as unsealing in the installation). Simulation based on the ALOHA model makes it possible to indicate the direction of pollution spread and constitutes the basis for taking action. And, the use of a drone allows to control contamination in real time and verify the probability of a threat occurring in a given area.

Faint calcium-rich transient from a double detonation of a 0.6 M⊙ carbon-oxygen white dwarf star

We have computed a 3D hydrodynamic simulation of the merger between a massive (0.4 M⊙) helium white dwarf (He WD) and a low-mass (0.6 M⊙) carbon-oxygen white dwarf (CO WD). Despite the low mass of the primary, the merger triggers a thermonuclear explosion as a result of a double detonation, producing a faint transient and leaving no remnant behind. This type of event could also take place during common-envelope mergers whenever the companion is a CO WD and the core of the giant star has a sufficiently large He mass. The spectra show strong Ca lines during the first few weeks after the explosion. The explosion only yields &lt; 0.01 M⊙ of 56Ni, resulting in a low-luminosity Type Ia supernova-like light curve that resembles the Ca-rich transients within this broad class of objects, with a peak magnitude of Mbol ≈ −15.7 mag and a rather slow decline rate of Δm15bol ≈ 1.5 mag. Both its light curve shape and spectral appearance resemble the appearance of Ca-rich transients, suggesting such mergers as a possible progenitor scenario for this class of events.

Different Response of the Ionospheric TEC and EEJ to Ultra‐Fast Kelvin Waves in the Mesosphere and Lower Thermosphere

AbstractWe studied the response of ionospheric total electron content (TEC) and equatorial electrojet (EEJ) to the ultra‐fast Kelvin wave (UFKW) at the equator in the mesosphere using zonal wind data obtained from TIMED Doppler Interferometer (TIDI), EEJ data over the monitoring station Jicamarca (12°S, 77°W) and global TEC maps. The least squares fitting method is utilized to perform a spectral analysis of zonal wind, EEJ and TEC. Our analysis results demonstrate that UFKW events can be divided into four categories: (a) UFKW events with both TEC and EEJ response; (b) UFKW events with TEC response but without EEJ response; (c) UFKW events with EEJ response but without TEC response; (d) UFKW events without neither TEC response nor EEJ response. The first type of UFKW events occur the most often and is generally thought to generate a response in EEJ at approximately 105–110 km through the dynamo effect. The polarization electric field associated with EEJ then produces a response in the ionospheric TEC through the fountain effect. The lack of EEJ response in the second type of UFKWs may be due to the influence of eastward background winds. We found that all UFKW events with EEJ response have a response in TEC. The fourth type of UFKWs have smaller amplitudes, shorter vertical wavelengths and longer periods, which make them more likely to dissipate and cannot propagate to higher altitudes. These UFKWs cannot propagate to the altitude of EEJ and produce a response in EEJ, much less in TEC.

An investigation of the associations between trauma exposure, racial stereotypes, and racist beliefs.

Given the concerning rise in hate crimes in recent years, it is critical to better understand factors associated with racist beliefs. As suggested by terror management theory (TMT), trauma exposure and posttraumatic stress symptoms (PTSS) may activate existential distress and anxiety, which may strengthen worldviews, including prejudiced beliefs (Greenberg & Kosloff, 2008; Weise et al., 2012). Although PTSS include negative alterations in beliefs about other people and the world, the connection between trauma and racist beliefs has not been investigated. There may also be key differences in terms of types of trauma exposure, such as interpersonal and noninterpersonal trauma, and racial beliefs. The present study aimed to determine: (a) whether cumulative trauma exposure and PTSS are associated with endorsement of racist perceptions and stereotypes regarding Black people, and (b) if interpersonal trauma is more strongly tied to prejudiced and stereotyped beliefs than noninterpersonal trauma among 277 White undergraduates (Mage = 23.33, SD = 6.11; 76.4% female). Neither cumulative trauma nor PTSS were found to be related to elevated racist beliefs or positive or negative stereotypes. However, noninterpersonal trauma exposure was associated with stronger endorsement of racist beliefs and negative stereotypes (ηp² = .03, .01). Surprisingly, interpersonal trauma exposure corresponded with lower racist beliefs (ηp² = .02). Noninterpersonal trauma exposure may thus activate TMT and strengthen prejudiced ideology, whereas interpersonal traumatic experiences and PTSS may not. More research is needed to better understand how types of traumatic events may relate to the development of prejudiced beliefs. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Unravelling the role of tropical cyclones in shaping present species distributions.

Driven by climate change, tropical cyclones (TCs) are predicted to change in intensity and frequency through time. Given these forecasted changes, developing an understanding of how TCs impact insular wildlife is of heightened importance. Previous work has shown that extreme weather events may shape species distributions more strongly than climatic averages; however, given the coarse spatial and temporal scales at which TC data are often reported, the influence of TCs on species distributions has yet to be explored. Using TC data from the National Hurricane Center, we developed spatially and temporally explicit species distribution models (SDMs) to examine the role of TCs in shaping present-day distributions of Puerto Rico's 10 Anolis lizard species. We created six predictor variables to represent the intensity and frequency of TCs. For each occurrence of a species, we calculated these variables for TCs that came within 500 km of the center of Puerto Rico and occurred within the 1-year window prior to when that occurrence was recorded. We also included predictor variables related to landcover, climate, topography, canopy cover and geology. We used random forests to assess model performance and variable importance in models with and without TC variables. We found that the inclusion of TC variables improved model performance for the majority of Puerto Rico's 10 anole species. The magnitude of the improvement varied by species, with generalist species that occur throughout the island experiencing the greatest improvements in model performance. Range-restricted species experienced small, almost negligible, improvements but also had more predictive models both with and without the inclusion of TC variables compared to generalist species. Our findings suggest that incorporating data on TCs into SDMs may be important for modeling insular species that are prone to experiencing these types of extreme weather events.

Does anticoagulation in combination with immunosuppressive therapy prevent recurrent thrombosis in Behçet's disease?

Vascular involvement in Behçet's disease (BD) occurs in up to 50% of patients. The main mechanism of thrombosis is inflammation. Thus, immunosuppressants (IS) are the mainstay of therapy, and adding anticoagulation (AC) is controversial. In daily practice, we observed that patients who received AC in combination with IS experienced less recurrent thrombosis and decided to investigate our BD patients retrospectively. We hypothesized that adding AC to immunosuppressive therapy may lower the risk of recurrent thrombosis. Treatment at the time of first or recurrent thrombotic events was recorded. Events under the only IS and IS + AC treatments were compared. There were 40 patients (33 males). The most common types of first vascular events were deep vein thrombosis (77.5%) followed by pulmonary embolism (PE) (52.5%). One patient did not receive any treatment. Among the 39 patients, 32 received glucocorticoid and at least one of the azathioprine, or cyclophosphamide, anti-TNF, 5 received monotherapy with azathioprine, 1 received monotherapy with corticosteroid, and the remaining 1 received monotherapy with cyclophosphamide. In total, 22 patients (55%) experienced 27 recurrent venous thromboembolism (VTE) events. Two (7.4%) events while only on AC, 2 (7.4%) events while on AC + IS, and 15 (55.5%) events occurred while on only IS. Eight (19.6%) patients were not receiving any treatment during relapses. The recurrence rate was statistically significantly lower in the IS + AC treatment group compared to IS alone. In conclusion, IS are the mainstay of treatment for BD, and adding AC may help to lower the recurrence risk of thrombotic events.

High Peak Current Lightning and the Production of Elves

AbstractElves are observed as expanding rings of light in the UV and visible optical bands. They are produced when electromagnetic pulses from lightning discharges interact with the lower parts of the ionosphere. Elves are well known to be associated with high peak current lightning discharges. Here, we use data from the Modular Multi‐spectral Imaging Array (MMIA) of the Atmosphere‐Space Interactions Monitor (ASIM), and search for observations of Elves when high peak current lightning discharges are detected by Vaisala's Global Lightning Detection network GLD360. We present two groups of events; high peak current detections associated with Elves and high peak current detections not associated with Elves. To understand why some current pulses with high peak currents do not produce observable Elves, we investigate and compare the lightning activity occurring before these two types of events, in terms of both the number of lightning discharges detected by GLD360 and the peak currents of the preceding discharges. Our results, using data from GLD360, suggest that current pulses with peak currents above |120| kA tend to produce Elves nearly always, regardless of the preceding lightning activity. For current pulses with peak currents between |70| and |120| kA, the number of observed Elves might be affected by the preceding lightning activity, or is the result of the characteristics of the storm cells that produce the Elve.

A New Evapotranspiration-Based Drought Index for Flash Drought Identification and Monitoring

Flash droughts, a type of extreme event characterized by the sudden onset and rapid intensification of drought conditions with severe impacts on ecosystems, have become more frequent in recent years due to global warming. The drought index is an effective way to monitor drought and mitigate its negative impact on human production and life. This study presents a new flash drought identification and monitoring method based on the evapotranspiration-based drought index, i.e., the evaporative stress percentile (ESP). This ESP-based method considers both the rate of the rapid intensification and each phase of flash drought development, which allows it to be used quantitative assessment of flash drought characteristics including detailed information on the onset, development, termination, and intensity. The ESP is evaluated using the soil moisture percentile (SMP) derived from the GLDAS-Noah soil moisture data. The results show that there was good agreement between the ESP and SMP across most of China, with correlation coefficient values above 0.8 and MAE values below 10 percentile/week. The ESP was then used to identify flash droughts in China and compared with the Precipitation Anomaly Percentage (PAP) and the SMP for three cases of typical flash drought events in three different regions and years with different land covers. It demonstrates the robustness of the ESP for detecting flash droughts in different geographical regions, for different land cover types, and for different climatic characteristics. This method is applied to characterize historical flash droughts in 1979–2018 in China, and the results show that flash droughts in China occur most frequently in the transitional climate zone between humid and arid regions in Northern China. This study contributes to a better understanding of flash drought development and supports to decision-makers in providing early warnings for flash droughts.

Event recognition method based on feature synthesizing for a zero-shot intelligent distributed optical fiber sensor

Phase-sensitive optical time domain reflectometer (Φ-OTDR) is an emergent distributed optical sensing system with the advantages of high localization accuracy and high sensitivity. It has been widely used for intrusion identification, pipeline monitoring, under-ground tunnel monitoring, etc. Deep learning-based classification methods work well for Φ-OTDR event recognition tasks with sufficient samples. However, the lack of training data samples is sometimes a serious problem for these data-driven algorithms. This paper proposes a novel feature synthesizing approach to solve this problem. A mixed class approach and a reinforcement learning-based guided training method are proposed to realize high-quality feature synthesis. Experiment results in the task of eight event classifications, including one unknown class, show that the proposed method can achieve an average classification accuracy of 42% for the unknown class and obtain its event type, meanwhile achieving a 74% average overall classification accuracy. This is 29% and 7% higher, respectively, than those of the ordinary instance synthesizing method. Moreover, this is the first time that the Φ-OTDR system can recognize a specific event and tell its event type without collecting its data sample in advance.

Adverse events associated with the delivery of telerehabilitation: A scoping review protocol.

This scoping review aims to map the existing research on adverse events during the delivery of telerehabilitation. Telerehabilitation, a subset of telemedicine, has gained traction during the COVID-19 pandemic as a means to deliver rehabilitation services remotely. However, there exists a research gap as there has yet to be any scoping review, systematic review, or meta-analysis published to identify and summarize the current primary research on adverse events related to telerehabilitation as a whole. It is important to understand how adverse events, such as falls during physiotherapy or aspiration pneumonia during speech language pathology sessions, are associated with telerehabilitation delivery. This will help to identify key limitations for optimizing telerehabilitation delivery by allowing for the development of key risk-mitigation measures and quality indicators. It can also help improve the uptake of telerehabilitation among clinicians and patients. This review aims to fill this research gap by conducting a search of published literature on adverse events in telerehabilitation. Anticipated key findings of this scoping review include identifying the characteristics and frequencies of adverse events during telerehabilitation, the patient populations and types of telerehabilitation associated with the most adverse events, and the quality of reporting of adverse events. The review follows the Joanna Briggs Institute (JBI) methodological framework and adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. The review protocol has been registered and published on Open Science Framework. A comprehensive search strategy was implemented across multiple databases (MEDLINE ALL, EMBASE, APA PsycINFO, CENTRAL, and CINAHL). All stages (screening, extraction, and synthesis) will be conducted in duplicate and independently, with data extraction following the TIDieR framework, along with authors, year of publication (before or after COVID), population and sample size, and specific mode/s of telerehabilitation delivery. For synthesis, data will be summarized quantitatively using numerical counts and qualitatively via content analysis. The data will be grouped by intervention type and by type of adverse event. This scoping review will include qualitative and quantitative studies published between 2013 and 2023, written in English, and conducted in any geographic area. All modes of telerehabilitation delivery (asynchronous, synchronous, or hybrid) will be included. Systematic reviews, meta-analyses, commentaries, protocols, opinion pieces, conference abstracts, and case series with fewer than five participants will be excluded.

Prompt for extraction: Multiple templates choice model for event extraction

Event Extraction (EE) is an essential task in natural language processing that aims to mine events occurring in event mentions represent events using event records, which usually consist of event types, trigger words, argument elements corresponding to roles in the event types. Recently, prompt-based generative models have been developed to extract events. However, these prompt-based generative studies have ignored the fact that the strong language comprehension capability of the pre-trained language model (PLM) can analyze extract the potential role relationships in multiple templates for more information that can help extract argument elements. To determine the extended templates that can help the model for event extraction, we propose the multiple template choice model (MTCM), which designs an extended event type mining module to automatically mine the extended event types in the event mention uses the templates corresponding to the extended event types to interact with the template, corresponding to the currently to-be-extracted event type of event mention, in a multi-template information interaction, which gives the model more information guides the PLM for event extraction. To validate our model, we used two widely used datasets in the event extraction domain, ACE2005-EN ERE-EN. The experimental results show that our model achieves state-of-the-art performance on the ACE2005-EN dataset significantly improves the ERE dataset. In addition, according to the results, our model can be effectively adapted to low-resource environments.