Low Identification Rate Research Articles

An early economic evaluation of WireSafe™ to prevent guidewire retention in central venous catheter procedures

Background Guidewire retention (GWR) poses a patient safety risk during Central Venous Catheter (CVC) insertions and is listed as a ‘Never Event’ by the National Health Service England (NHSE). WireSafe™ is an intervention to prevent GWR, but its cost-effectiveness is uncertain. This study is an early economic evaluation comparing WireSafe™ to standard care from a UK healthcare perspective. Methods We conducted (i) a primary analysis of GWR related NHSE Never Events data in England between 2016 and 2020; and (ii) a cost-utility analysis, including healthcare costs and quality-adjusted life years (QALYS) for populations receiving CVC-insertions. We applied a cost-effectiveness threshold of £30,000 per QALY and considered three WireSafe™ costing scenarios (£18.50, £4.50, and £2.50). Results NHSE Never Events data showed 61 GWR cases, averaging 1 per month. Most incidents (92%) were identified during hospital stays, with one serious adverse outcome reported (peri-arrest). In a population of 200,000, we estimate WireSafe™ would prevent 59.92 wire retentions, 5.61 procedural adverse events, 0.3 cardiac adverse events, and 0.19 deaths, improving QALYs by 4.87. In the base case analysis WireSafe™ was not cost-effective and had an economically justifiable price of £2.44. There were high levels of uncertainty in the lowest cost-scenario (ICER 95% credible interval: Dominant; £793,398). Conclusions The health benefits of WireSafe™ are limited due to low GWR rates and high identification rates, making WireSafe™ viable only at low costs. Future research should prioritise obtaining more precise estimates of these parameter values which are key determinants of cost-effectiveness.

Transient Ischaemic attack Emergency Referral (TIER): randomised feasibility trial results

BackgroundEarly assessment of patients with suspected transient ischaemic attack (TIA) is crucial to provision of effective care, including initiation of preventive therapies and identification of stroke mimics. Many patients with...

Research on Welding Defect Classification Methods Based on Convolutional Neural Networks

To address the issues of low detection and identification rates and inaccurate classification of welding defects, this paper proposes a welding defect classification method based on convolutional neural networks. By modifying the ResNet101 backbone network and incorporating a multi-head attention mechanism (MHA), the method enhances the focus on regions of interest in welding defect images, thereby improving the ability to extract defect features. This approach reduces the chances of missed detections and false positives. By analyzing the effects of different optimizers and dynamic factors on recall (M) and precision (N), the optimal optimizer and dynamic factor are identified. The algorithm is validated on the JPEGWD dataset. Experimental results show that the recall (M) reaches 0.9634, representing a 4.88% improvement, and the precision (N) reaches 0.9875, representing a 3.81% improvement. The mean average precision (mAP) is 0.9817, reflecting a 5.98% improvement. These results effectively enhance the recognition rate of welding defects and reduce the probability of missed detections and false positives.

A Compton scattering background subtraction method of gamma energy spectrum based on Gaussian function convolution

Due to the influence of the Compton effect and the relatively close gamma energy emitted during certain nuclide decays, gamma-ray spectrum analysis often faces challenges, such as a high count of Compton scattering continuum and overlapping peaks, which leading to some errors in energy spectrum analysis and low nuclide identification rates. This study proposes a spectrum analysis technique that uses Gaussian convolution to effectively eliminate background counts induced by Compton scattering. To minimize calculation errors in variance, the variance error is decreased from 2.98% to 0.84% through the utilization of a binary method for adjusting the transformation scale. Simulation results show that the average relative error of peak area calculations is less than 10.78%, and in the range of signal to noise ratio (SNR) greater than 22.5 dB, the differences between the standard peak center position and the peak center position measured by the Gaussian convolution method are less than 0.049%. The measured spectrum experiments were also carried out to confirm that the average relative error of peak area calculation is around 7.63%, and the relative error between the Gaussian convolution method and Gamma Vision peak-seeking result is around 0.18%. These results demonstrate that this method can effectively eliminate the Compton scattering background counts in the gamma spectrum, obtain the peak position and peak area of the full energy peak, and facilitate the accurate identification of the weak peak in the gamma spectrum.

Prevalence and genomic characteristics of becAB-carrying Clostridium perfringens strains

Clostridium perfringens, as a foodborne pathogen, can cause various intestinal diseases in both humans and animals according to its repertoire of toxins. In recent years, a multitude of studies have highlighted its threat to infants and young children. C. perfringens carries numerous toxins, with the newly identified BEC toxin confirmed as the second toxin to cause diarrheal illness, after CPE. However, the global dissemination of C. perfringens strains carrying becAB genes, which encode BEC toxins, has not been extensively studied. Following epidemiological surveillance of the prevalence of C. perfringens from different sources in various provinces of China, we identified two becAB-carrying strains and one strain carrying a sequence similar to becAB from distinct provinces and sources. When combined with genomic analysis of other becAB-carrying C. perfringens strains from public databases, we found that becAB was present in strains from different lineages. Our analysis of the plasmid and genetic environment corroborates previous findings on becAB-carrying strains, confirming that it currently achieves horizontal transmission through one type of evolutionarily conserved Pcp plasmid. This study provides a comprehensive analysis of the prevalence and transmission patterns of the newly emerged toxin gene locus, becAB, in C. perfringens. Despite the relatively low identification rate of becAB-carrying strains, their potential impact requires ongoing surveillance and investigation of their features, particularly their antimicrobial resistance.

On the possible advantages of combining small lineups with instructions that discourage guessing-based selection

The primary argument for including large numbers of known-to-be innocent fillers in lineups is that guessing-based selections are dispersed among a large number of lineup members, leading to low innocent-suspect identification rates. However, a recent study using the two-high threshold eyewitness identification model has demonstrated advantages of smaller lineups at the level of the processes underlying the observable responses. Participants were more likely to detect the presence of the culprit and less likely to select lineup members based on guessing in smaller than in larger lineups. Nonetheless, at the level of observable responses, the rate of innocent-suspect identifications was higher in smaller compared to larger lineups due to the decreased dispersion of guessing-based selections among the lineup members. To address this issue, we combined smaller lineups with lineup instructions insinuating that the culprit was unlikely to be in the lineup. The goal was to achieve a particularly low rate of guessing-based selections. These lineups were compared to larger lineups with neutral instructions. In two experiments, culprit-presence detection occurred with a higher probability in smaller compared to larger lineups. Furthermore, instructions insinuating that the culprit was unlikely to be in the lineup reduced guessing-based selection compared to neutral instructions. At the level of observable responses, the innocent-suspect identification rate did not differ between smaller lineups with low-culprit-probability instructions and larger lineups with neutral instructions. The rate of culprit identifications was higher in smaller lineups with low-culprit-probability instructions than in larger lineups with neutral instructions.

Clinical Characteristics and Microorganisms Isolated in Community-Acquired Pneumonia in the COVID-19 Period.

Community-acquired pneumonia is a leading cause of mortality and hospital admissions. The aetiology remains unknown in 30-65% of the cases. Molecular tests are available for multiple pathogen detection and are under research to improve the causal diagnosis. We carried out a prospective study to describe the clinical characteristics and aetiology of community-acquired pneumonia during the COVID-19 pandemic and to assess the diagnostic effectivity of the microbiological tests, including a molecular test of respiratory pathogens (FilmArray™ bioMérieux). From the 1st of February 2021 until the 31st of March 2022, 225 patients were included. Failure in microorganism identification occurred in approximately 70% of patients. Streptococcus pneumoniae was the most common isolate. There were 5 cases of viral pneumonia. The tested FilmArray exhibited a low positivity rate of 7% and mainly aided in the diagnosis of viral coinfections. Despite our extensive diagnostic protocol, there is still a low rate of microorganism identification. We have observed a reduction in influenza and other viral pneumoniae during the COVID-19 pandemic. Having a high NEWS2 score on arrival at the emergency department, an active oncohematological disease or chronic neurological conditions and a positive microbiological test result were related to worse outcomes. Further research is needed to determine the role of molecular tests in the microbiological diagnosis of pneumonia.

Research on UAV Flight Parameter Identification Method Based on Launch Force and Airspeed.

Flight parameters are crucial criteria for UAV control, playing a significant role in ensuring the safe and efficient completion of missions. Launch force and airspeed information are key parameters in the early and middle stages of flight, serving as important data for monitoring the UAV's flight status. In response to challenges such as weak launch force, low identification rates, small airspeed, and low recognition accuracy in UAVs, a method for identifying UAV flight parameters based on launch force and airspeed is proposed. From the aspect of launch force identification, a recognition method based on a low-g value accelerometer information source is proposed, utilizing a 'multi-level time window + threshold' approach. For airspeed identification, an optimization method for airspeed measurement under the Kalman filter architecture is introduced. A device for airspeed measurement based on pressure sensors is designed, and the recommended installation position is determined through simulation. Furthermore, the feasibility and robustness of the proposed launch force identification and airspeed measurement optimization methods are validated through simulation. Finally, the effectiveness of the design is verified through centrifuge and wind tunnel experiments. This research provides technical support for the identification of the launch force and airspeed measurement in UAVs.

Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet

Aiming at the influence of the fundamental frequency and its harmonics in transformer vibration signals on fault signals, which cause a low fault identification rate and degradation of classification model performance, a new strategy is proposed for fault diagnosis using periodic map spectrum feature maps as input features. In this study, the optimal decomposition parameters were first found adaptively using the VMD improved by the positive cosine optimisation algorithm; then, the transformer vibration signal was modally decomposed, and the periodic map spectrum features were plotted according to the differences in the energy distribution of the different modal components at different frequencies. Finally, the GoogLeNet classification model with the improved attention mechanism assigned different weights to the feature maps in both spatial and channel dimensions to improve the classification accuracy and achieve transformer fault diagnosis. The experimental results verified the validity of the adopted feature map and the proposed model, and the accuracy was significantly improved to 99.04% compared with the traditional GoogLeNet, which is valuable for engineering applications.

The “Sniffin’ Kids-PT” test: A smell test variant for Portuguese children

ObjectiveOlfactory tests tailored for children are essential, as diagnosing olfactory dysfunction at these ages can be challenging. The 16-item “Sniffin’ Sticks” is reliable and easy to perform. To the best of our knowledge, there is currently no validated olfactory test for the Portuguese pediatric population. This study aimed to adapt and validate the “Sniffin’ Sticks” olfactory test for the Portuguese pediatric population. MethodsBetween August 2020 and September 2021, 354 children aged between 6 and 17years old enrolled in the study, 336 healthy children with a normal sense of smell, and 18 anosmic children with Kallmann syndrome. The study consisted of two parts. Firstly, the “Sniffin’ Sticks” olfactory identification test was applied to healthy children and the odors with statistically significant low identification rates were excluded. A modified version of “Sniffin’ Sticks” was defined and named “Sniffin’ Kids-PT” test. Secondly, normative data were assessed and test-retest and validation tests were performed. ResultsApple and Cloves odors were identified with a low rate and were excluded from the “Sniffin’ Sticks” olfactory test. In the modified 14-item “Sniffin’ Kids-PT”, scoring <6 (from 6–8years old), <7 (from 9–11years old) or <8 (from 12–14years old and 15–17years old) was indicative of olfactory dysfunction. The test–retest reliability was good (r=0.81; P<0.001) and the differences between scores of healthy children and anosmic children were statistically significant (U213=13.00; P<0.001). ConclusionThe modified “Sniffin’ Kids-PT” is a reliable test to discriminate between normosmia and olfactory dysfunction in Portuguese children over 5years old.

Characteristics of drawing deficits in people with aphasia: Differences between symbolic and realistic drawn objects.

Pictures drawn by people with aphasia (PWA) are often more challenging to understand than those drawn by healthy people. There are two types of objects: those that tend to be drawn symbolically (symbolically drawn objects-SOs) and those that are likely to be drawn realistically (realistically drawn objects-ROs). To compare the identification rate and number of misunderstanding types between SOs and ROs drawn by PWA and healthy controls (HCs). To reveal trends in the misunderstandings of drawings by PWA, and to identify the language or cognitive abilities related to the identification rate of pictures drawn by PWA. We designed a drawing task involving SOs and ROs. A total of 18 PWA and 30 HCs completed the task, and respondents identified the drawings. The identification rate and number of misunderstandings were analysed with two-way analysis of variance (ANOVA) including group (PWA and HCs) and object type (SOs and ROs). The misunderstandings were divided into four categories varying in semantic and morphological similarity; these ratios were examined with a chi-square test. The relationships of language and cognitive abilities with the identification rate were investigated with multiple regression analyses. There was a significant effect of the interaction between group and object type on the identification rate (F(1.1387) = 3.90, Mean Squared Error (MSE) = 4139.67, p = 0.04): the identification rates for ROs were lower in the PWA than in the HCs. For the number of misunderstanding types, an interaction was observed between group and object type (F(1.56) = 8.26, MSE = 26.93, p < 0.01): the number of misunderstanding types for ROs in the PWA was greater than that in the HCs. The misunderstanding patterns differed between ROs and SOs (χ2(3) = 694.30, p < 0.001, V = 0.37). ROs were semantically related, whereas SOs were morphologically related. The identification rates of ROs and SOs were correlated only with Kanji writing scores (ROs: β = 3.66, p = 0.01; SOs: β = 6.57, p < 0.01). In drawings by the PWA, SOs had a higher identification rate, while ROs had a lower identification rate and a greater variety of misunderstandings. SOs may increase drawing motivation. Interventions to improve the identifiability of SOs and ROs should reflect each character. Identification rates were correlated only with Kanji writing scores. The PWA, whose native language was Japanese and had preserved Kanji writing abilities, and their communication abilities may be increased through drawing. What is already known on the subject PWA often have impaired drawing abilities and draw pictures that third parties misinterpret. Some objects tend to be drawn symbolically, and some are drawn realistically. However, it is not clear whether there is a difference between these types of drawings depicted by PWA in identifiability and the tendency to be misunderstood by ordinary people. In addition, the relationships between language or cognitive abilities and the identification rate of drawn pictures are not clear. What this paper adds to the existing knowledge The identification rate differed between SOs and ROs. In drawings by PWA, SOs had a higher identification rate, while ROs had a lower identification rate and the greatest variety of misunderstandings. Approximately half of the misunderstandings were related to the target object. SOs tended to be confused with morphologically related objects, while ROs tended to be confused with semantically related objects. Identification rates were correlated only with Kanji writing scores. What are the potential or actual clinical implications of this work? To motivate PWA's drawing, it is suitable to begin with SOs. Examining drawing ability from the perspective of SOs and ROs increases the chance of identifying drawing ability. PWA whose native language is Japanese and have preserved Kanji writing abilities may be able to increase their communication abilities through drawing.

Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with this technology is the relatively low rate of phosphopeptide identification during data analysis. This limitation hampers the full realization of the potential offered by shotgun phosphoproteomics. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19% to 46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

A star identification algorithm for rolling shutter exposure based on Hough transform

The star identification algorithm usually identifies stars by angular distance matching. However, under high dynamic conditions, the rolling shutter effect distorts the angular distances between the measured and true star positions, leading to plethoric false matches and requiring complex and time-consuming verification for star identification. Low identification rate hinders the application of low-noise and cost-effective rolling shutter image sensors. In this work, we first study a rolling shutter distortion model of angular distances between stars, and then propose a novel three-stage star identification algorithm to identify distorted star images captured by the rolling shutter star sensor. The first stage uses a modified grid algorithm with adaptive error tolerance and an expanded pattern database to efficiently eliminate spurious matches. The second stage performs angular velocity estimation based on Hough transform to verify the matches that follow the same distortion pattern. The third stage applies a rolling shutter error correction method for further verification. Both the simulation and night sky image test demonstrate the effectiveness and efficiency of our algorithm under high dynamic conditions. The accuracy of angular velocity estimation method by Hough transform is evaluated and the root mean square error is below 0.5 (°)/s. Our algorithm achieves a 95.7% identification rate at an angular velocity of 10 (°)/s, which is much higher than traditional algorithms.

Open-set face recognition with maximal entropy and Objectosphere loss

Open-set face recognition characterizes a scenario where unknown individuals, unseen during the training and enrollment stages, appear on operation time. This work concentrates on watchlists, an open-set task that is expected to operate at a low false-positive identification rate and generally includes only a few enrollment samples per identity. We introduce a compact adapter network that benefits from additional negative face images when combined with distinct cost functions, such as Objectosphere Loss (OS) and the proposed Maximal Entropy Loss (MEL). MEL modifies the traditional Cross-Entropy loss in favor of increasing the entropy for negative samples and attaches a penalty to known target classes in pursuance of gallery specialization. The proposed approach adopts pre-trained deep neural networks (DNNs) for face recognition as feature extractors. Then, the adapter network takes deep feature representations and acts as a substitute for the output layer of the pre-trained DNN in exchange for an agile domain adaptation. Promising results have been achieved following open-set protocols for three different datasets: LFW, IJB-C, and UCCS as well as state-of-the-art performance when supplementary negative data is properly selected to fine-tune the adapter network.

Clinical characteristics of hospitalized patients with Nocardia genus detection by metagenomic next generation sequencing in a tertiary hospital from southern China

ObjectiveAs an opportunistic pathogen, Nocardia often occurring in the immunocompromised hosts. As the unspecifc clinical presentation and low identification rate of the culture dependent methods, Nocardia infection may be under-diagnosis. Recent study have reported physicians could benefit from metagenomic next-generation sequencing (mNGS) in Nocardia diagnosis. Herein, we present patients with a positive detection of nocardiosis in mNGS, aiming to provide useful information for an differential diagnosis and patients management.MethodsA total of 3756 samples detected for mNGS from March 2019 to April 2022 at the Fifth Affifiliated Hospital of Sun Yat-sen University, were screened. Clinical records, laboratory finding, CT images and mNGS results were reviewed for 19 patients who were positive for Nocardia genus.ResultsSamples from low respiratory tract obtained by bronchoscope took the major part of the positive (15/19). 12 of 19 cases were diagnosis as Nocardiosis Disease (ND) and over half of the ND individuals (7/12) were geriatric. Nearly all of them (10/12) were immunocompetent and 2 patients in ND group were impressively asymptomatic. Cough was the most common symptom. Nocardia cyriacigeorgica (4/12) was more frequently occurring in ND, followed by Nocardia abscessus (3/12). There are 3 individuals detected more than one kind of Nocardia species (Supplementary table 1). Except one with renal failure and one allergic to sulfamethoxazole, all of them received co-sulfonamide treatment and relieved eventually.ConclusionOur study deciphered the clinical features of patients with positive nocardiosis detected by mNGS. Greater attention should be paid to the ND that occurred in the immunocompetent host and the geriatric. Due to the difficulties in establishing diagnosis of Nocardiosis disease, mNGS should play a much more essential role for a better assessment in those intractable cases. Co-sulfonamide treatment should still be the first choice of Nocardiosis disease.

Delivery and Transcriptome Assessment of an In Vitro Three-Dimensional Proximal Tubule Model Established by Human Kidney 2 Cells in Clinical Gelatin Sponges.

The high prevalence of kidney diseases and the low identification rate of drug nephrotoxicity in preclinical studies reinforce the need for representative yet feasible renal models. Although in vitro cell-based models utilizing renal proximal tubules are widely used for kidney research, many proximal tubule cell (PTC) lines have been indicated to be less sensitive to nephrotoxins, mainly due to altered expression of transporters under a two-dimensional culture (2D) environment. Here, we selected HK-2 cells to establish a simplified three-dimensional (3D) model using gelatin sponges as scaffolds. In addition to cell viability and morphology, we conducted a comprehensive transcriptome comparison and correlation analysis of 2D and 3D cultured HK-2 cells to native human PTCs. Our 3D model displayed stable and long-term growth with a tubule-like morphology and demonstrated a more comparable gene expression profile to native human PTCs compared to the 2D model. Many missing or low expressions of major genes involved in PTC transport and metabolic processes were restored, which is crucial for successful nephrotoxicity prediction. Consequently, we established a cost-effective yet more representative model for in vivo PTC studies and presented a comprehensive transcriptome analysis for the systematic characterization of PTC lines.

Peak Detection Algorithm for Mass Spectrometry Integrating Weighted Continuous Wavelet Transform with Particle Swarm Optimization-Based Otsu

Peak detection is an important step in mass spectrometry as accurately identifying characteristic peaks is key to data analysis. In order to address the issue of false peak detection, while simultaneously ensuring accurate detection of weak and overlapped peaks, this paper introduces an improved algorithm for mass spectrometry integrating weighted continuous wavelet transform with particle swarm optimization-based Otsu (WWTPO). The algorithm applies the weighted continuous wavelet transform (WCWT) to compress the frequency spectrum signal into a smaller scale range, which allows for the acquisition of more distinct and informative peak information. Moreover, the algorithm employs the particle swarm optimization (PSO) algorithm to iteratively evaluate the optimal image segmentation threshold, which addresses the challenge of inaccurate Otsu image segmentation. The method was applied to detect simulated peaks as well as matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) datasets. The performance evaluation was conducted using receiver operating characteristic (ROC) curves, F1 measure and F-scores. Through comparison with continuous wavelet transform (CWT) and genetic algorithm-based threshold segmentation (WSTGA), multi-scale peak detection (MSPD) and CWT and image segmentation (CWT-IS), the results demonstrate that WWTPO exhibits excellent performance in peak detection. The determination of 4-isopropyltoluene also demonstrates that WWTPO has excellent practical application. This method not only maintains a low false peak identification rate but also detects more weak peaks and overlapping peaks, further improving the accuracy and efficiency of peak detection in mass spectrometry.

Classification and Identification of Apple Leaf Diseases and Insect Pests Based on Improved ResNet-50 Model

Automaticidentification and prevention of leaf diseases and insect pests on fruit crops represent a key trend in the development of smart agriculture. In order to address the limitations of existing models with low identification rates of apple leaf diseases and insect pests, a novel identification model based on an improved ResNet-50 architecture was proposed, which incorporated the coordinate attention (CA) module and weight-adaptive multi-scale feature fusion (WAMFF) to enhance the ResNet-50’s image feature extraction capabilities. Transfer learning and online data enhancement are employed to boost the model’s generalization ability. The proposed model achieved a top-1 accuracy rate of 98.32% on the basis of AppleLeaf9 datasets, which is 4.58% higher than the value from the original model, and the improved model can effectively improve the localization of lesion features. Furthermore, compared with mainstream deep networks, such as AlexNet, VGG16, DenseNet, MNASNet, and GoogLeNet on the same dataset, the top-1 accuracy rate increased by 7.3%, 3.19%, 4.98%, 6.04% and 3.87%, respectively. The experimental results demonstrate that the improved model is effective in improving the identification accuracy of apple leaf diseases and insect pests and enhancing the model’s effective feature extraction capabilities.

Evaluation of the Jichi Medical University diverticular hemorrhage score in the clinical management of acute diverticular bleeding with emergency or elective endoscopy: A pilot study.

Emergency endoscopic hemostasis for colonic diverticular bleeding is effective in preventing serious consequences. However, the low identification rate of the bleeding source makes the procedure burdensome for both patients and providers. We aimed to establish an efficient and safe emergency endoscopy system. We prospectively evaluated the usefulness of a scoring system (Jichi Medical University diverticular hemorrhage score: JD score) based on our experiences with past cases. The JD score was determined using four criteria: CT evidence of contrast agent extravasation, 3 points; oral anticoagulant (any type) use, 2 points; C-reactive protein ≥1 mg/dL, 1 point; and comorbidity index ≥3, 1 point. Based on the JD score, patients with acute diverticular bleeding who underwent emergency or elective endoscopy were grouped into JD ≥3 or JD <3 groups, respectively. The primary and secondary endpoints were the bleeding source identification rate and clinical outcomes. The JD ≥3 and JD <3 groups included 35 and 47 patients, respectively. The rate of bleeding source identification, followed by the hemostatic procedure, was significantly higher in the JD ≥3 group than in the JD <3 group (77% vs. 23%, p <0.001), with a higher JD score associated with a higher bleeding source identification rate. No significant difference was observed between the groups in terms of clinical outcomes, except for a higher incidence of rebleeding at one-month post-discharge and a higher number of patients requiring interventional radiology in the JD ≥3 group than in the JD <3 group. Subgroup analysis showed that successful identification of the bleeding source and hemostasis contributed to a shorter hospital stay. We established a safe and efficient endoscopic scoring system for treating colonic diverticular bleeding. The higher the JD score, the higher the bleeding source identification, leading to a successful hemostatic procedure. Elective endoscopy was possible in the JD <3 group when vital signs were stable.

Special Education Representation Trends Vary by Language Status: Evidence of Underrepresentation in Tennessee

Using U.S. state-level data, we report unadjusted and adjusted odds ratio of special education (SPED) trends in Tennessee from 2009 to 2019 for students in Grades 3 to 8 by three language groups: native English speakers (NES), English-proficient bilinguals (EPB), and Current English learners (Current EL). We report trends across all SPED disability categories and across five prevalent disability categories (specific learning disability, specific language impairment, intellectual disability, other health impairments, and autism). The cross-sectional analytic sample included 812,783 students from 28 districts that met the SPED risk ratio threshold set by the state. Results revealed that, compared with NES students, both EPB and Current EL students were generally less likely to receive SPED services, suggesting evidence of language status disparities in SPED representation. Furthermore, findings varied depending on whether adjustments were made to generate odds ratios, especially for higher-incidence disabilities (specific learning disability, specific language impairment, and intellectual disability). Finally, the most severe evidence of underrepresentation was in lower-incidence disabilities (other health impairments and autism). Our results underscore the need for further examination into low rates of SPED identification among learners whose first language is not English (EPB and Current EL). We discuss the contextualized research, practice, and policy implications of our findings.