Event Classifications Research Articles

Populism and the rule of law: The importance of institutional legacies

AbstractExisting work sees populist governments undermining the rule of law because they seek to dismantle institutional constraints on their personalistic plebiscitarian rule. We argue that populist rulers pose a greater threat to legal impartiality, equality, and compliance when they face a legacy of weak rule of law. We find empirical support for this assertion after applying synthetic control methods to a cross‐country sample that includes up to 51 populist events spanning the period from 1920 to 2019. Our results remain consistent across a range of robustness checks including, the consideration of a set of contextual variables that can potentially determine the capacity of populist governments to sweep away institutional constraints, different populist event classifications, and different ways of measuring the rule of law. In countries, like the United States, with a robust rule of law tradition, the deleterious impact of populists on institutions will be limited but not negligible.

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.

Asymmetric response of global drought and pluvial detection to the length of climate epoch

Standardized indices (SIs) are routinely used to assess hydroclimate extreme events. However, to what extent such assessment is robust considering the choice of climate epoch (that is, a specific time period with explicit data length) is still not clear especially when developing new SIs. In this study, we use multiple SIs to systematically investigate the sensitivities of global drought and pluvial detection to the length of climate epoch by separating the parameter fitting step from the entire SI calculation process. We find that the sensitivity of event classifications to data length is more similar between Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Moisture Anomaly Index (SZI) at the six-month time scale, compared to that of Standardized Precipitation Index (SPI). Moreover, despite the symmetric definition of droughts and pluvials, the choice of climate epoch has asymmetric impacts on event detection. Detection of SZI-based droughts is more sensitive to data length than pluvials and generally requires longer series (additional 10 years compared to a 30-year climatology) to accurately fit the tailed distribution, especially over a few hot spot regions, including South Africa, South Asia and Oceania.

PO-03-081 THE AUDIT OF FOCUSONTM SERVICE ACCURACY AND TIME BENEFIT ANALYSIS

FocusOnTM (FO) is a service provided by Medtronic Ltd. designed to triage and report cardiac device transmissions in order of clinical urgency using a traffic light system. The benefit of this is improved efficiency of arrhythmia detection as clinically urgent reports are given priority resulting in reduced workload on cardiac centres, particularly within implantable loop recorder (ILR) follow-up. This study aims to assess the efficiency of the FO service and the clinical accuracy of FO ILR reports. Two hundred and twenty-seven ILR patients with Medtronic Linq I & II devices were randomly sampled from the clinical centre’s FO database. Four hundred and seventy-one reports were reviewed and rated between zero and two, within the following categories: rate, rhythm, morphology, miscellaneous details and AF description (table 1). Additional data was collected from the time of transmission to FO to dismissal at the clinical centre to assess time efficiency. None of the reports scored zero for incorrect reporting. Sixty-four percent of reports scored one for requiring more detail in at least one of the reporting categories. Pauses, symptoms, false positives and undersensing event classifications made up the majority of this group. Thirty-six percent of reports mostly including AF/AT, tachyarrythmias and bradyarrhythmias scored two, requiring none to minimal improvement (figure 1). The mean time from ILR transmission to FO reporting totalled eight hours and FO reporting to dismissal at the clinical centre was six hours. Totalling an average transmission to clinical action of fourteen hours which is significantly shorter than pre-FO usage which averaged at ninety-one hours. The results suggest FO is a trustworthy reporting service as all abnormalities were appropriately highlighted within the study cohort. It also highlights the specific areas that required minimal improvement. Amendments to the reporting detail could see improved reporting efficiency and a reduction in the demand for secondary review by senior CS. The speed of review was also very impressive further suggesting FO service is efficient in reviewing and escalating transmissions, providing valuable patient benefits. Future research into this area could see that non-clinical staff at clinical centres are utilised to assist with the workflow.

Typology, Severity, and Outcomes of Adverse Events Related to Angiographic Equipment-A Ten-Year Analysis of the FDA MAUDE Database.

Purpose: Angiographic equipment is a key component of healthcare infrastructure, used for endovascular procedures throughout the body. The literature on adverse events related to this technology is limited. The purpose of this study was to analyze adverse events related to angiographic devices from the US Food and Drug Administration's Manufacturer and User Facility Device Experience (MAUDE) database. Methods: MAUDE data on angiographic imaging equipment from July 2011 to July 2021 were extracted. Qualitative content analysis was performed, a typology of adverse events was derived, and this was used to classify the data. Outcomes were assessed using the Healthcare Performance Improvement (HPI) and Society of Interventional Radiology (SIR) adverse event classifications. Results: There were 651 adverse events reported. Most were near misses (67%), followed by precursor safety events (20.5%), serious safety events (11.2%), and unclassifiable (1.2%). Events impacted patients (42.1%), staff (3.2%), both (1.2%), or neither (53.5%). The most common events associated with patient harm were intra-procedure system shut down, foot pedal malfunction, table movement malfunction, image quality deterioration, patient falls, and fluid damage to system. Overall, 34 (5.2%) events were associated with patient death; 18 during the procedure and 5 during patient transport to another angiographic suite/hospital due to critical failure of equipment. Conclusion: Adverse events related to angiographic equipment are rare; however, serious adverse events and deaths have been reported. This study has defined a typology of the most common adverse events associated with patient and staff harm. Increased understanding of these failures may lead to improved product design, user training, and departmental contingency planning.

Using Artificial Intelligence Methods to Classify Different Seismic Events

Abstract The classification of seismic events is crucial for monitoring underground nuclear explosions and regional unnatural seismic events. To classify tectonic earthquakes, explosions, and mining-induced earthquakes, we established 36- and 60-dimensional network-averaged datasets and single-station datasets through feature extraction and spectral amplitude analysis. Using different artificial intelligence (AI) methods, including the support vector machine (SVM), extreme gradient boosting (XGBoost), long short-term memory network (LSTM), residual neural network, and long short-term memory fully convolutional network (LSTM-FCN), we constructed two-class and three-class models, analyzed the change in the classification with epicentral distances, and evaluated the generalizability of different classifiers. The results showed that the accuracies of different AI models with the feature extraction dataset were higher than those achieved with the spectral amplitude dataset, indicating that the feature extraction method can more clearly highlight the differences between different types of seismic events. The accuracies with the network-averaged dataset were 5%–8% higher than that achieved using the single-station dataset. The earthquake and mining-induced earthquake classifiers constructed by different AI methods had the best performance, followed by the earthquake and explosion classifier, and the explosion and mining-induced earthquake classifier, with average accuracies of 97.4%–98.4%, 96.5%–97.6%, and 88.8%–90.6%, respectively. In the model generalization evaluation, the test accuracies and F1-Scores of the two-class models with the 36-dimensional network-averaged dataset exceeded 90%. Among the five AI methods, XGBoost and LSTM both performed well in classification of different datasets, indicating that these models have good application prospects for seismic event classifications.

Partition pooling for convolutional graph network applications in particle physics

Convolutional graph networks are used in particle physics for effective event reconstructions and classifications. However, their performances can be limited by the considerable amount of sensors used in modern particle detectors if applied to sensor-level data. We present a pooling scheme that uses partitioning to create pooling kernels on graphs, similar to pooling on images. Partition pooling can be used to adopt successful image recognition architectures for graph neural network applications in particle physics. The reduced computational resources allow for deeper networks and more extensive hyperparameter optimizations. To show its applicability, we construct a convolutional graph network with partition pooling that reconstructs simulated interaction vertices for an idealized neutrino detector. The pooling network yields improved performance and is less susceptible to overfitting than a similar network without pooling. The lower resource requirements allow the construction of a deeper network with further improved performance.

Machine learning methods for identification and classification of events in ϕ-OTDR systems: a review.

The phase sensitive optical time-domain reflectometer (φ-OTDR), or in some applications called distributed acoustic sensing (DAS), has been a popularly used technology for long-distance monitoring of vibrational signals in recent years. Since φ-OTDR systems usually operate in complicated and dynamic environments, there have been multiple intrusion event signals and also numerous noise interferences, which have been a major stumbling block toward the system's efficiency and effectiveness. Many studies have proposed different techniques to mitigate this problem mainly in φ-OTDR setup upgrades and improvements in data processing techniques. Most recently, machine learning methods for event classifications in order to help identify and categorize intrusion events have become the heated spot. In this paper, we provide a review of recent technologies from conventional machine learning algorithms to deep neural networks for event classifications aimed at increasing the recognition/classification accuracy and reducing nuisance alarm rates (NARs) in φ-OTDR systems. We present a comparative analysis of the current classification methods and then evaluate their performance in terms of classification accuracy, NAR, precision, recall, identification time, and other parameters.

Characterization of soil moisture response patterns and hillslope hydrological processes through a self-organizing map

Abstract. Hydrologic events can be characterized as particular combinations of hydrological processes on a hillslope scale. To configure hydrological mechanisms, we analyzed a dataset using an unsupervised machine learning algorithm to cluster the hydrologic events based on the dissimilarity distances between the weighting components of a self-organizing map (SOM). The time series of soil moisture was measured at 30 points (at 10 locations with three different depths) for 356 rainfall events on a steep, forested hillslope between 2007 and 2016. The soil moisture features for hydrologic events can be effectively represented by the antecedent soil moisture, soil moisture difference index, and standard deviation of the peak-to-peak time between rainfall and soil moisture response. Five clusters were delineated for hydrologically meaningful event classifications in the SOM representation. The two-dimensional spatial weighting patterns in the SOM provided more insights into the relationships between rainfall characteristics, antecedent wetness, and soil moisture response at different locations and depths. The distinction of the classified events could be explained by several rainfall features and antecedent soil moisture conditions that resulted in different patterns attributable to combinations of hillslope hydrological processes, vertical flow, and lateral flow along either surface or subsurface boundaries for the upslope and downslope areas.

Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Array

New deep learning techniques present promising new analysis methods for Imaging Atmospheric Cherenkov Telescopes (IACTs) such as the upcoming Cherenkov Telescope Array (CTA). In particular, the use of Convolutional Neural Networks (CNNs) could provide a direct event classification method that uses the entire information contained within the Cherenkov shower image, bypassing the need to Hillas parameterise the image and allowing fast processing of the data.Existing work in this field has utilised images of the integrated charge from IACT camera photomultipliers, however the majority of current and upcoming generation IACT cameras have the capacity to read out the entire photosensor waveform following a trigger. As the arrival times of Cherenkov photons from Extensive Air Showers (EAS) at the camera plane are dependent upon the altitude of their emission and the impact distance from the telescope, these waveforms contain information potentially useful for IACT event classification.In this test-of-concept simulation study, we investigate the potential for using these camera pixel waveforms with new deep learning techniques as a background rejection method, against both proton and electron induced EAS. We find that a means of utilising their information is to create a set of seven additional 2-dimensional pixel maps of waveform parameters, to be fed into the machine learning algorithm along with the integrated charge image. Whilst we ultimately find that the only classification power against electrons is based upon event direction, methods based upon timing information appear to out-perform similar charge based methods for gamma/hadron separation. We also review existing methods of event classifications using a combination of deep learning and timing information in other astroparticle physics experiments.

Defining heat waves and extreme heat events using sub-regional meteorological data to maximize benefits of early warning systems to population health

BackgroundExtreme heat events have been consistently associated with an increased risk of hospitalization for various hospital diagnoses. Classifying heat events is particularly relevant for identifying the criteria to activate early warning systems. Heat event classifications may also differ due to heterogeneity in climates among different geographic regions, which may occur at a small scale. Using local meteorological data, we identified heat waves and extreme heat events that were associated with the highest burden of excess hospitalizations within the County of San Diego and quantified discrepancies using county-level meteorological criteria. MethodsEighteen event classifications were created using various combinations of temperature metric, percentile, and duration for both county-level and climate zone level meteorological data within San Diego County. Propensity score matching and Poisson regressions were utilized to ascertain the association between heat wave exposure and risk of hospitalization for heat-related illness and dehydration for the 1999–2013 period. We estimated both relative and absolute risks for each heat event classification in order to identify optimal definitions of heat waves and extreme heat events for the whole city and in each climate zone to target health impacts. ResultsHeat-related illness differs vastly by level (county or zone-specific), definition, and risk measure. We found the county-level definitions to be systematically biased when compared to climate zone definitions with the largest discrepancy of 56 attributable hospitalizations. The relative and attributable risks were often minimally correlated, which exemplified that relative risks alone are not adequate to optimize heat waves definitions. ConclusionsDefinitions based on county-level defined thresholds do not provide an accurate picture of the observed health effects and will fail to maximize the potential effectiveness of heat warning systems. Absolute rather than relative risks are a more appropriate measure to define the set of criteria to activate early warnings systems and thus maximize public health benefits.

Impact of Adding Aspirin to Direct Oral Anticoagulant Therapy without an Apparent Indication

Introduction: The direct oral anticoagulants (DOACs: apixaban, dabigatran, edoxaban and rivaroxaban) may be used for the treatment and secondary prevention of venous thromboembolism (VTE) and for the prevention of stroke in the setting of non-valvular atrial fibrillation (NVAF). Adding aspirin (ASA) to a DOAC is often appropriate after acute coronary syndromes or percutaneous coronary intervention. However, many patients receive oral anticoagulation and ASA without a clear need for combination therapy. Current data, largely from patients treated with warfarin and ASA, suggest that the addition of ASA may increase bleeding risk without reducing thrombotic events. However, it is uncertain if this applies to patients on DOACs with ASA, and patients on combination DOAC+ASA therapy were not well represented in clinical trials. We sought to evaluate the impact of adding ASA to DOAC therapy for patients without an apparent indication for combination therapy. Methods: We conducted a registry-based cohort study of adults on DOAC therapy for NVAF or VTE followed at 6 anticoagulation clinics in Michigan between January 2009 to June 2019, recruited through the Michigan Anticoagulation Quality Improvement Initiative (MAQI2). Patients were excluded if they had a history of heart valve replacement, recent myocardial infarction, or less than 3 months of follow-up. Two propensity matched cohorts (DOACs vs. DOAC+ASA) of patients were analyzed based on aspirin use at the time of study enrollment. The primary outcome was any new bleeding event. Secondary outcomes included new episodes of arterial or VTE, bleeding event type (fatal, life threatening, major, clinically relevant non-major, non-major bleeding (CRNMB), and intracranial or intraspinal), and death. Random chart audits were done to confirm the accuracy of the abstracted data. Results: Of the study cohort of 2,045 patients, 647 (31.6%) patients without a clear indication for ASA received ASA with a DOAC. We compared two groups of 639 matched patients. Patient demographics, co-morbidities, and concurrent medications were well-balanced after propensity score matching. After an average of 15.2 months of follow-up we found that patients on combination therapy (DOAC+ASA) had a significantly higher rate of bleeding compared to patients on DOAC monotherapy (319 bleeding events vs. 261 bleeding events, P=0.02; 0.41 bleeds per patient vs. 0.33 bleeds per patient). This difference was largely driven by CRNMB events (151 with DOAC+ASA vs. 109 with DOAC monotherapy, P=0.01), with the only 2 fatal bleeding events observed with DOAC monotherapy. We did not observe a significant difference between the groups in other bleeding event classifications. Bleeding sites were most commonly cutaneous, gastrointestinal, and genitourinary. Observed rates of thrombosis (stroke, VTE, myocardial infarction, or other) were similar between the groups (19 events with DOAC+ASA vs. 18 events for DOAC monotherapy, P=NS). Patients on combination therapy had more emergency room visits and hospitalizations but these differences were not statistically significant. Conclusion: Patients on oral anticoagulation for VTE or NVAF with a DOAC, without a clear need for ASA, experienced more bleeding events with the addition of ASA compared to DOAC monotherapy, without an apparent improvement in the incidence of thrombosis. Further study is needed to assess if DOAC+ASA is safer than warfarin+ASA, to compare the outcomes of the individual DOACs, and to confirm these findings in a larger cohort. Until such assessment is complete, clinicians should carefully consider the need to add aspirin in patients on DOAC therapy. Disclosures Kaatz: Portola: Honoraria; Pfizer: Honoraria; Janssen: Honoraria, Research Funding; Bristol Myers Squibb: Honoraria. Kline-Rogers:AC Forum: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria; QUANTUM-AF: Membership on an entity's Board of Directors or advisory committees. Sood:Bayer: Research Funding. Froehlich:Fibromuscular Dysplasia Society: Research Funding; Janssen: Honoraria; Pfizer: Honoraria; Boehringer-Ingelheim: Honoraria; Merck: Honoraria; Blue Cross Blue Shield of Michigan: Research Funding; Novartis: Honoraria. Barnes:Portola: Honoraria; Janssen: Honoraria; Blue Cross Blue Shield of Michigan: Research Funding; Bristol Myers Squib: Honoraria; AMAG Pharmaceuticals: Honoraria; Pfizer/Bristol Myers Squib: Research Funding; Pfizer: Honoraria.

Deep Gaussian Mixture-Hidden Markov Model for Classification of EEG Signals

Electroencephalography (EEG) signals are complex dynamic phenomena that exhibit nonlinear and nonstationary behaviors. These characteristics tend to undermine the reliability of existing hand-crafted EEG features that ignore time-varying information and impair the performances of classification models. In this paper, we propose a novel method that can automatically capture the nonstationary dynamics of EEG signals for diverse classification tasks. It consists of two components. The first component uses an autoregressive-deep variational autoencoder model for automatic feature extraction, and the second component uses a Gaussian mixture-hidden Markov model for EEG classification with the extracted features. We compare the performance of our proposed method and the state-of-the-art methods in two EEG classification tasks, subject, and event classification. Results show that our approach outperforms the others by averages of $\text{15}\%\pm \text{6.3}$ (p-value $ ) and $\text{22}\%\pm \text{5.7}$ (p-value $ ) for subject and event classifications, respectively.

Insights from surgeons’ eye-movement data in a virtual simulation surgical training environment: effect of experience level and hand conditions

ABSTRACTToday, with the advancements in the eye-tracking technology, it has become possible to follow surgeons’ eye movements while performing surgical tasks. Despite the availability of studies providing a better understanding of surgeons’ eye movements, research in the particular field of endoneurosurgery is very limited. Analysing surgeons’ eye-movement data can provide general insights into how to improve surgical education programmes. In this study, four simulation-based task-oriented endoscopic surgery training scenarios were developed and implemented by 23 surgical residents using three different hand conditions; dominant, non-dominant, and both. The participants’ recorded eye data comprised fixation number, fixation duration, saccade number, saccade duration, pursuit number, pursuit duration, and pupil size. This study has two main contributions: First, it reports on the eye-movement behaviours of surgical residents, demonstrating that novice residents tended to make more fixations and saccades than intermediate residents. They also had a higher fixation duration and followed the objects more frequently compared to the intermediates. Furthermore, hand conditions significantly affected the eye movements of the participants. Based on these results, it can be concluded that eye-movement data can be used to assess the skill levels of surgical residents and would be an important measure to better guide trainees in surgical education programmes. The second contribution of this study is the eye-movement event classifications of 10 different algorithms. Although the algorithms mostly provided similar results, there were a few conflicted values for some classifications, which offers a clue as to how researchers can utilise these algorithms with low sampling frequency eye trackers.

Is There Really a Benefit to Net Clinical Benefit in Testing Antithrombotics?

The evaluation of novel antithrombotics requires a careful assessment of both efficacy and safety. Assessment of efficacy typically relies on a composite of cardiovascular death, myocardial infarction, and stroke (major adverse cardiac events) that are supposedly hard clinical outcomes with objective definitions that can be adjudicated in blinded fashion. There are well-known problems related to using composite outcome measures1 such as the effect of a new treatment on components of the composite being different in magnitude or in direction, or the fact that these components have markedly different severity, leading some to propose the weighting of different events based on their associated rates of disability or subsequent attributable mortality. In recent years, for instance, there has been increasing concern that some myocardial infarctions are actually rather clinically minor events in which minute amounts of myocardial injury can be detected by ultrasensitive high-sensitivity troponin. Assessment of safety generally relies on measuring rates of bleeding, categorized according to one or several of the many existing bleeding event classifications, although some antithrombotic agents can have off-target effects (such as dyspnea in the case of ticagrelor). The desire to assess the balance of risk and benefit in a single quantitative measure, and the wish to create a composite outcome encompassing both efficacy and safety, as well, have led to the computation of new composite variables: net clinical benefit (ie, the difference between the number of major adverse cardiac events avoided and the price to pay in terms of excess bleeding events) or net adverse clinical …

Laboratory and field evaluation of a small form factor head impact sensor in un-helmeted play

Head impact sensors are increasingly used to quantify the frequency and magnitude of head impacts in sports. A dearth of information exists regarding head impact in un-helmeted sport, despite the substantial number of concussions experienced in these sports. This study evaluated the performance of one small form factor head impact sensor in both laboratory and field environments. In laboratory tests, sensor performance was assessed using a Hybrid III headform and neck. The headform assembly was mounted on a low-friction sled and impacted with three sports balls over a range of velocities (10–31 m/s) at two locations and from three directions. Measures of linear and angular acceleration obtained from the small form factor wireless sensor were compared to measures of linear and angular acceleration obtained by wired sensors mounted at the headform center of mass. Accuracy of the sensor varied inversely with impact magnitude, with relative differences across test conditions ranging from 0.1% to 266.0% for peak linear acceleration and 4.7% to 94.6% for peak angular acceleration when compared to a wired reference system. In the field evaluation, eight male high school soccer players were instrumented with the head impact sensor in seven games. Video of the games was synchronized with sensor data and reviewed to determine the number of false positive and false negative head acceleration event classifications. Of the 98 events classified as valid by the sensor, 20.5% (20 impacts) did not result from contact with the ball, another player, the ground or player motion and were therefore considered false positives. Video review of events classified as invalid or spurious by the sensor found 77.8% (14 of 18 impacts) to be due to contact with the ball, another player or player motion and were considered false negatives.

Operational Experience Data Analysis on Events Related to Maintenance at Nuclear Power Plants

An in-depth analysis of maintenance-related events was performed by screening four different databases. The events cover the period 2002 to 2013. A total of 921 events were selected for analysis. An examination of the selected events resulted in their classification into nine categories or groups (e.g., plant state, type of maintenance, affected component, root cause, etc.). For further analyses, the categories were divided into families and, if necessary, into subfamilies. One of the event classifications was according to the type of maintenance (periodic, predictive, planned, and corrective). The data analysis indicated that 47% of the events reported were related to periodic maintenance. The main affected components were valves (with 33% of the events), followed by electric power components (23%). The main root causes observed are maintenance performed incorrectly (27%), deficiencies in written procedures or documents (19%), and deficiencies in management or organization (17%). Regarding the impact on safety, the dominant family is potential effects on safety function (57%), followed by significant effect on operation (20%).

Ontology-based Vaccine and Drug Adverse Event Representation and Theory-guided Systematic Causal Network Analysis toward Integrative Pharmacovigilance Research.

Compared with controlled terminologies (e.g., MedDRA, CTCAE, and WHO-ART), the community-based Ontology of AEs (OAE) has many advantages in adverse event (AE) classifications. The OAE-derived Ontology of Vaccine AEs (OVAE) and Ontology of Drug Neuropathy AEs (ODNAE) serve as AE knowledge bases and support data integration and analysis. The Immune Response Gene Network Theory explains molecular mechanisms of vaccine-related AEs. The OneNet Theory of Life treats the whole process of a life of an organism as a single complex and dynamic network (i.e., OneNet). A new "OneNet effectiveness" tenet is proposed here to expand the OneNet theory. Derived from the OneNet theory, the author hypothesizes that one human uses one single genotype-rooted mechanism to respond to different vaccinations and drug treatments, and experimentally identified mechanisms are manifestations of the OneNet blueprint mechanism under specific conditions. The theories and ontologies interact together as semantic frameworks to support integrative pharmacovigilance research.

Safety incident reporting in emergency radiology: analysis of 1717 safety incident reports.

The aim of this article is to describe the incidence and types of safety reports logged in the radiology safety incident reporting system in our emergency radiology section over an 8-year period. Electronic incident reporting system of our institute was searched for the variables in emergency radiology. All reports from April 2006 to June 2014 were included and deindentified. The following event classifications were investigated in radiography, CT, and MRI modalities: diagnostic test orders, ID/documentation/consent, safety/security/conduct, service coordination, surgery/procedure, line/tube, fall, medication/IV safety, employee general incident, environment/equipment, adverse drug reaction, skin/tissue, and diagnosis/treatment. A total of 881,194 emergency radiology examinations were performed during the study period, 1717 (1717/881,194 = 0.19 %) of which resulted in safety reports. Reports were classified into 14 different categories, the most frequent of which were "diagnostic test orders" (481/1717 = 28 % total incident reports), "medication/IV safety" (302/1717 = 18 % total incident reports), and "service coordination" (204/1717 = 12 % total incident reports). X-ray had the highest report rate (873/1717 = 50 % total incident reports), followed by CT (604/1717 = 35 % total incident reports) and MRI (240/1717 = 14 % total incident reports). Forty-six percent of safety incidents (789/1717) caused no harm and did not reach the patient, 36 % (617/1717) caused no harm but reached the patient, 18 % (308/1717) caused temporary or minor harm/ damage, and less than 1 % caused permanent or major harm/ damage or death. Our study shows an overall safety incident report rate of 0.19 % in emergency radiology including radiography, CT, and MRI modalities. The most common safety incidents were diagnostic test orders, medication/IV safety, and service coordination.

Epidemiological evaluation of notifications of environmental events in the State of São Paulo, Brazil.

Increasing urbanization across the globe, combined with an increased use of chemicals in various regions, contributes to several environmental events that influence environmental health. Measures that identify environmental factors and events should be introduced to facilitate epidemiological investigations by health services. The Brazilian Ministry of Health published a new list of notifiable diseases on 25 January 2011 and introduced environmental events as a new category of notifiable occurrences. The Center for Epidemiologic Surveillance in State of Sao Paulo, Brazil, created an online notification system that highlights “environmental events”, such as exposure to chemical contaminants, drinking water with contaminants outside of the recommended range, contaminated air, and natural or anthropogenic disasters. This paper analyzed 300 notifications received between May 2011 and May 2012. It reports the number of notifications with event classifications and analyzes the events relating to accidents with chemical substances. This paper describes the characteristics of the accidents that involved chemical substances, methods used, types of substances, exposed population, and measures adopted. The online notification of environmental events increases the analysis of the main events associated with diseases related to environmental chemicals; thus, it facilitates the adoption of public policies to prevent environmental health problems.