Standard Metrics Research Articles

New bag of features using reinforcement Aquila optimization and weighted Bayesian Gaussian mixture modelling for dental images

AbstractDentistry diseases are worldwide concern with the fact that 5% of medical budget is spent on it. Dental diagnosis requires frequent visits to clinic and multiple personal check‐ups by an expert. This delays the process of diagnosis as well as introduces the danger of oral infection spread. Automation is boon to speed up the process of diagnosis, reduce expert's involvement and help in handling volume of patients. In situations like pandemic as seen in this decade, computer based automatic systems in health have proven their importance and necessity. For dental diagnosis, images are useful tool for better anatomical views and accurate decision of treatment. However, manual dental analysis increases load on dentists for initial check‐up that can be easily performed with automated systems or self‐kits. Oral cavity is a dental illness, if not identified and treated on time may lead to other serious ailments. This work presents a framework that performs tooth image classification for cavity and non‐cavity with new bag of features (NBoF) method. NBoF method is an attempt to improve the performance of bag of features (BoF) using the proposed reinforcement Aquila optimization (RAO) and weighted Bayesian Gaussian mixture modelling (WBGMM). An analysis of the performance of the NBoF using the proposed RAO and WBGMM is conducted using standard metrics. The comparative study of results proves that the proposed NBoF method outperforms the existing state‐of‐the‐art algorithms.

Decentralized IoT System Based on Blockchain and Homomorphic Technologies

Decentralization within IoT eliminates the need to use distributed networks within IoT to communicate only with servers that may face difficulties related to the internet, vulnerabilities, DDOS, or hijacks, merging blockchain with IoT converted the IoT system into a decentralized with many benefits and outcomes from this conversion. An encryption scheme homomorphic technique (HE) is a method that encrypts the cipher data without the need to decrypt it,paillier encryption method is used. This paper aims to propose a system that integrates Paillier cryptosystem homomorphic technology with IoT and lightweight blockchain technology to provide decentralization to the IoT environment and improve security. The proposed system results in improving the IoT device’s work environment by solving the main challenges of security using blockchain, privacy using homomorphism, and data volume using blockchain. The data set used to implement and evaluate the proposed system is industrial internet of things data The dataset used in this paper is generated via machine industry 4.0 Storage System status which represents the system failure and work status. this system is evaluated using standard metrics used to evaluate the blockchain effectiveness and time, resources consumed and shows better results in time and power consumption. Index Terms— internet of things, homomorphic, blockchain, IoT decentralization, IioT.

Preoperative Dehydration Predicts Adverse Events Following Anterior Cervical Discectomy and Fusion.

Anterior cervical discectomy and fusion (ACDF) is a common procedure for neck arthritis, typically alleviating pain and improving function. Preoperative dehydration has been correlated with postoperative infection, acute renal failure, deep vein thrombosis, and increased hospital length of stay. However, some studies have suggested that preoperative dehydration has a minimal relationship with postoperative outcomes, specifically in arthroplasty and lumbar surgery candidates. Patients who underwent ACDF from 2015 to 2020 as part of the American College of Surgeons National Surgical Quality Improvement Program database were identified. We excluded patients who presented with acute trauma. Dehydration was determined using the accepted definition of preoperative blood urea nitrogen to creatinine ratio greater than 20. Lengths of stay and 30-day postoperative adverse events were compared between dehydrated and nondehydrated cohorts, adjusting for baseline features using standard multivariate regression. We identified 14,932 patients, and 4206 (28.1%) of whom were preoperatively dehydrated. Dehydrated patients had significantly higher odds of wound, hematological, and pulmonary complications; Clavien-Dindo grade IV, delayed length of stay (>5 days); and a lower likelihood of being discharged home (P < 0.005), even after controlling for demographic features (eg, sex, age, body mass index, race, and ethnicity). Furthermore, linear regression suggested an overall half-day increased length of hospital stay for dehydrated patients (95% CI [0.36, 0.60], P < 0.001). Preoperative dehydration is common among ACDF surgery patients and appears to correlate with an increased risk of postoperative complications and prolonged length of hospital stay. Evaluation of a patient's hydration status from standard preoperative laboratory metrics can be employed for risk stratification, patient counseling, and timing of ACDF surgeries.

A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion

Higher-level autonomous driving necessitates the best possible execution of important moves under all conditions. Most of the accidents in recent years caused by the AVs launched by leading automobile manufacturers are due to inadequate decision-making, which is a result of their poor perceivance of environmental information. In today’s technology-bound scenarios, versatile sensors are used by AVs to collect environmental information. Due to various technical and natural calamities, the environmental information acquired by the sensors may not be complete and clear, due to which the AVs may misinterpret the information in a different context, leading to inadequate decision-making, which may then lead to fatal accidents. To overcome this drawback, effective preprocessing of raw sensory data is a mandatory task. Pre-processing the sensory data involves two vital tasks, namely data cleaning and data fusion. Since the raw sensory data are complex and exhibit multimodal characteristics, more emphasis is given to data preprocessing. Since more innovative models have been proposed for data cleaning, this study focused on data fusion. In particular, this study proposed a generic data fusion engine, which classifies different formats of sensory data and fuses them accordingly to improve accuracy. This study proposed a generic framework to fuse the text, image, and audio data. In the first stage of this research, an innovative hybrid model was proposed to fuse multispectral image and video data. Simple and efficient models to extract the salient image features were also proposed. The hybrid image fusion model that was proposed did not yield satisfactory outcomes when combining 3D point cloud data, and its performance declined when evaluating large datasets. To address this issue, the study expanded by introducing an advanced generative adversarial network (GAN) to transform the hybrid image fusion model into a machine learning model capable of handling substantial datasets. Additionally, customized kernel functions were suggested to fuse 3D point cloud data effectively. The performance of the proposed models was assessed using standard metrics and datasets, comparing them with existing popular models. The results revealed that the proposed image fusion model outperformed the other models.

Identifying Themes in Energy Poverty Research: Energy Justice Implications for Policy, Programs, and the Clean Energy Transition

Energy poverty affects numerous households across the globe and has several key implications and concerns for public health and social equity. Energy poverty is defined as “the lack of access to modern and affordable energy services”. Individuals or communities in energy poverty face limitations in accessing reliable, affordable, and sustainable energy. This review paper examines a focused subset of recent research on energy poverty highlighted by the “NSF 2026: Priorities and Research Needs for an Equitable Energy Transition” workshop and the United States Department of Energy’s Office of Energy Justice Policy and Analysis to help frame energy poverty’s impacts on policy, poverty alleviation, environmental impact, and social inequity. This review paper uses five themes to organize previous energy poverty work: (1) Energy Poverty and Justice Definitions and Metrics; (2) Behavioral Aspects of Energy Poverty; (3) Efficacy of Energy Assistance Programs; (4) Efficiency of Energy Efficiency Policy; (5) The Energy Transition and Environmental and Energy Justice. We found that the literature examined how comprehensive assessment of energy poverty requires going beyond standard statistics and metrics and must include an understanding of how underserved households interact with energy. We found strong optimism for the clean energy transition’s ability to significantly alleviate energy poverty, but only if policymakers include equity. Finally, we found that while there is plenty of work highlighting deficiencies there is a dearth of work examining successful implementations and how to replicate them which will be needed if the clean energy transition is to match its potential.

Deep learning for automated measurement of CSA related acromion morphological parameters on anteroposterior radiographs

BackgroundThe Critical Shoulder Angle Related Acromion Morphological Parameter (CSA- RAMP) is a valuable tool in the analyzing the etiology of the rotator cuff tears (RCTs). However, its clinical application has been limited by the time-consuming and prone to inter- and intra-user variability of the measurement process. ObjectivesTo develop and validate a deep learning algorithm for fully automated assessment of shoulder anteroposterior radiographs associated with RCTs and calculation of CSA-RAMP. MethodsRetrospective analysis was conducted on radiographs obtained from computed tomography (CT) scans and X-rays performed between 2018 and 2020 at our institution. The development of the system involved the utilization of digitally reconstructed radiographs (DRRs) generated from each CT scan. The system's performance was evaluated by comparing it with manual and semiautomated measurements on two separate test datasets: dataset I (DRRs) and dataset II (X-rays). Standard metrics, including mean average precision (AP), were utilized to assess the segmentation performance. Additionally, the consistency among fully automated, semiautomated, and manual measurements was comprehensively evaluated using the Pearson correlation coefficient and Bland-Altman analysis. ResultsA total of 1080 DRRs generated from 120 consecutive CT scans and 159 X-ray films were included in the study. The algorithm demonstrated excellent segmentation performance, with a mean AP of 57.67 and an AP50 of 94.31. Strong inter-group correlations were observed for all CSA-RAMP measurements in both test datasets I (automated versus manual, automated versus semiautomated, and semiautomated versus manual; r = [0.990–––0.997], P < 0.001) and dataset II (r = [0.984–––0.995], P < 0.001). Bland-Altman analysis revealed low bias for all CSA-RAMP measurements in both test datasets I and II, except for CD (with a maximum bias of 2.49%). ConclusionsWe have successfully developed a fully automated algorithm capable of rapidly and accurately measuring CSA-RAMP on shoulder anteroposterior radiographs. A consistent automated CSA- RAMP measurement system may accelerate powerful and precise studies of disease biology in future large cohorts of RCTs patients.

Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation Study.

Accurate projections of procedural case durations are complex but critical to the planning of perioperative staffing, operating room resources, and patient communication. Nonlinear prediction models using machine learning methods may provide opportunities for hospitals to improve upon current estimates of procedure duration. The aim of this study was to determine whether a machine learning algorithm scalable across multiple centers could make estimations of case duration within a tolerance limit because there are substantial resources required for operating room functioning that relate to case duration. Deep learning, gradient boosting, and ensemble machine learning models were generated using perioperative data available at 3 distinct time points: the time of scheduling, the time of patient arrival to the operating or procedure room (primary model), and the time of surgical incision or procedure start. The primary outcome was procedure duration, defined by the time between the arrival and the departure of the patient from the procedure room. Model performance was assessed by mean absolute error (MAE), the proportion of predictions falling within 20% of the actual duration, and other standard metrics. Performance was compared with a baseline method of historical means within a linear regression model. Model features driving predictions were assessed using Shapley additive explanations values and permutation feature importance. A total of 1,177,893 procedures from 13 academic and private hospitals between 2016 and 2019 were used. Across all procedures, the median procedure duration was 94 (IQR 50-167) minutes. In estimating the procedure duration, the gradient boosting machine was the best-performing model, demonstrating an MAE of 34 (SD 47) minutes, with 46% of the predictions falling within 20% of the actual duration in the test data set. This represented a statistically and clinically significant improvement in predictions compared with a baseline linear regression model (MAE 43 min; P<.001; 39% of the predictions falling within 20% of the actual duration). The most important features in model training were historical procedure duration by surgeon, the word "free" within the procedure text, and the time of day. Nonlinear models using machine learning techniques may be used to generate high-performing, automatable, explainable, and scalable prediction models for procedure duration.

Towards a Paradigm Shift on Mapping Muddy Waters with Sentinel-2 Using Machine Learning

The quality of drinking water is a critical factor for public health and the environment. Inland drinking water reservoirs are essential sources of freshwater supply for many communities around the world. However, these reservoirs are susceptible to various forms of contamination, including the presence of muddy water, which can pose significant challenges for water treatment facilities and lead to serious health risks for consumers. In addition, such reservoirs are also used for recreational purposes which supports the local economy. In this work, we show as a proof-of-concept that muddy water mapping can be accomplished with machine learning-based semantic segmentation constituting an extra source of sediment-laden water information. Among others, such an approach can solve issues including (i) the presence/absence, frequency and spatial extent of pollutants (ii) generalization and expansion to unknown reservoirs (assuming a curated global dataset) (iii) indications about the presence of other pollutants since it acts as their proxy. Our train/test approach is based on 13 Sentinel-2 (S-2) scenes from inland/coastal waters around Europe while treating the data as tabular. Atmospheric corrections are applied and compared based on spectral signatures. Muddy water and non-muddy water samples are taken according to expert knowledge, S-2 scene classification layer, and a combination of normalized difference indices (NDTI and MNDWI) and are evaluated based on their spectral signature statistics. Finally, a Random Forest model is trained, fine-tuned and evaluated using standard classification metrics. The experiments have shown that muddy water can be detected with high enough discrimination capacity, opening the door to more advanced image-based machine learning techniques.

An advanced ensemble modeling approach for predicting carbonate reservoir porosity from seismic attributes

This study uses a machine learning (ML) ensemble modeling approach to predict porosity from multiple seismic attributes in one of the most promising Main Dolomite hydrocarbon reservoirs in NW Poland. The presented workflow tests five different model types of varying complexity: K-nearest neighbors (KNN), random forests (RF), extreme gradient boosting (XGB), support vector machine (SVM), single layer neural network with multilayer perceptron (MLP). The selected models are additionally run with different configurations originating from the pre-processing stage, including Yeo–Johnson transformation (YJ) and principal component analysis (PCA). The race ANOVA method across resample data is used to tune the best hyperparameters for each model. The model candidates and the role of different pre-processors are evaluated based on standard ML metrics – coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). The model stacking is performed on five model candidates: two KNN, two XGB, and one SVM PCA with a marginal role. The results of the ensemble model showed superior accuracy over single learners, with all metrics (R2 0.890, RMSE 0.0252, MAE 0.168). It also turned out to be almost three times better than the neural net (NN) results obtained from commercial software on the same testing set (R2 0.318, RMSE 0.0628, MAE 0.0487). The spatial distribution of porosity from the ensemble model indicated areas of good reservoir properties that overlap with hydrocarbon production fields. This observation completes the evaluation of the ensemble technique results from model metrics. Overall, the proposed solution is a promising tool for better porosity prediction and understanding of heterogeneous carbonate reservoirs from multiple seismic attributes.

International consensus recommendations on face transplantation: A 2-step Delphi study

Face transplantation is a viable reconstructive approach for severe craniofacial defects. Despite the evolution witnessed in the field, ethical aspects, clinical and psychosocial implications, public perception, and economic sustainability remain the subject of debate and unanswered questions. Furthermore, poor data reporting and sharing, the absence of standardized metrics for outcome evaluation, and the lack of consensus definitions of success and failure have hampered the development of a “transplantation culture” on a global scale. We completed a 2-round online modified Delphi process with 35 international face transplant stakeholders, including surgeons, clinicians, psychologists, psychiatrists, ethicists, policymakers, and researchers, with a representation of 10 of the 19 face transplant teams that had already performed the procedure and 73% of face transplants. Themes addressed included patient assessment and selection, indications, social support networks, clinical framework, surgical considerations, data on patient progress and outcomes, definitions of success and failure, public image and perception, and financial sustainability. The presented recommendations are the product of a shared commitment of face transplant teams to foster the development of face transplantation and are aimed at providing a gold standard of practice and policy.

A Perceptual Measure for Deep Single Image Camera and Lens Calibration.

Image editing and compositing have become ubiquitous in entertainment, from digital art to AR and VR experiences. To produce beautiful composites, the camera needs to be geometrically calibrated, which can be tedious and requires a physical calibration target. In place of the traditional multi-image calibration process, we propose to infer the camera calibration parameters such as pitch, roll, field of view, and lens distortion directly from a single image using a deep convolutional neural network. We train this network using automatically generated samples from a large-scale panorama dataset, yielding competitive accuracy in terms of standard l2 error. However, we argue that minimizing such standard error metrics might not be optimal for many applications. In this work, we investigate human sensitivity to inaccuracies in geometric camera calibration. To this end, we conduct a large-scale human perception study where we ask participants to judge the realism of 3D objects composited with correct and biased camera calibration parameters. Based on this study, we develop a new perceptual measure for camera calibration and demonstrate that our deep calibration network outperforms previous single-image based calibration methods both on standard metrics as well as on this novel perceptual measure. Finally, we demonstrate the use of our calibration network for several applications, including virtual object insertion, image retrieval, and compositing.

Assessing the Impact of a Note-Writing Session and Standardized Note Template on Medical Student Note Length and Quality.

To determine whether a formal note-writing session and note template for medical students (MS) during the Core Clerkship in Pediatrics (CCP) increases note quality, shortens note length, and decreases time of documentation. In this single site, prospective study, MS participating in an 8-week CCP received a didactic session on note-writing in the electronic health record (EHR) and utilized EHR template developed for the study. We assessed note quality [measured by Physician Documentation Quality Instrument-9 (PDQI-9)], note length and note documentation time in this group compared to MS notes on the CCP in the prior academic year. We used descriptive statistics and Kruskal-Wallis tests for analysis. We analyzed 121 notes written by 40 students in the control group and 92 notes writing by 41 students in the intervention group. Notes from the intervention group were more "up to date," "accurate," "organized," and "comprehensible" compared to the control group (p=0.02, p=0.04, p=0.01, and p=0.02, respectively). Intervention group notes received higher cumulative PDQI-9 scores compared to the control group (median score 38 (IQR 34-42) vs 36 (IQR 32-40) out of 45 total, p=0.04). Intervention group notes were approximately 35% shorter than the control group notes (median 68.5 lines vs 105 lines, p <0.0001) and were signed earlier than control group notes (median file time 316 minutes vs 352 minutes, p=0.02). The intervention successfully decreased note length, improved note quality based on standardized metrics, and reduced time to completion of note documentation. An innovative note writing curriculum and accompanying standardized note template improved the outcomes of medical student progress notes in multiple domains, including timeliness, accuracy, organization, and overall quality." The intervention also significantly decreased note length and time to note completion.

Occupational Performance 1 Year After a Distal Radius Fracture From the Perspective of the International Classification of Functioning, Disability and Health.

Distal radius fractures (DRFs) frequently compromise independent functioning. To analyze occupational performance post-DRF using the Canadian Occupational Performance Measure (COPM) and related interviews, guided by the International Classification of Functioning, Disability and Health (ICF). COPM score was compared with standard metrics: Patient-Reported Wrist Evaluation (PRWE) and Participation Behavior Questionnaire (PBQ). Through a semistructured COPM interview, participants highlighted self-care, productivity, and leisure. They completed the PRWE and PBQ, linking results to ICF sets. Prospective cohort. Outpatient hand surgery clinic at a trauma center. Patients (N = 120), 1 yr post-DRF. Incorporated COPM, PRWE, and PBQ, linked to ICF. Analysis identified 73 codes, aligning with 30 ICF Hand Conditions codes. Main concerns related to self-care (15.21%), leisure (12.16%), and productivity (16.22%). Major challenges pertained to domestic (67.00%) and civic life (64.53%) within ICF. Occupational performance was positively correlated with participation (r = .62) and inversely with disability (r = -.62). DRFs result in varied occupational challenges that are not always reflected in standard measures. The COPM offers a comprehensive insight into post-DRF patient challenges, emphasizing the value of diverse clinical assessment approaches. What This Article Adds: This study highlights the importance of a holistic approach in occupational therapy for DRF patients, revealing that standard measurements might overlook key challenges that they face. By adopting broader evaluative methods, occupational therapists can better address patient-specific needs and enhance their rehabilitation outcomes.

New boosting approaches for improving cluster-based undersampling in problems with imbalanced data

Class unbalanced datasets are frequently encountered in a variety of areas including health, security, and finance. Often these datasets create bias in the supervised learning models trained for the prediction task. One of the most successful techniques to handle imbalanced data is undersampling. Experiments demonstrate that cluster-based undersampling improves over random undersampling in many cases. In this paper, we propose three new boosting approaches to improve the performance of cluster-based undersampling technique: (i) inject unlabeled data into training data for improved clustering; (ii) keep the instances close to cluster boundary and centroid while undersampling and (iii) remove the majority samples in the neighborhood of minority data in each cluster. We experimented with our boosting methods over 49 standard benchmark datasets and analyzed the performances in terms of standard evaluation metrics. Experimental results suggest these boosting techniques are promising and significantly improve over cluster-based undersampling strategies.

A survey of computational methods and databases for lncRNA-miRNA interaction prediction.

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are two prevalent non-coding RNAs in current research. They play critical regulatory roles in the life processes of animals and plants. Studies have shown that lncRNAs can interact with miRNAs to participate in post-transcriptional regulatory processes, mainly involved in regulating cancer development, metastatic progression, and drug resistance. Additionally, these interactions have significant effects on plant growth, development, and responses to biotic and abiotic stresses. Deciphering the potential relationships between lncRNAs and miRNAs may provide new insights into our understanding of the biological functions of lncRNAs and miRNAs, and the pathogenesis of complex diseases. In contrast, gathering information on lncRNA-miRNA interactions (LMIs) through biological experiments is expensive and time-consuming. With the accumulation of multi-omics data, computational models are extremely attractive in systematically exploring potential LMIs. To the best of our knowledge, this is the first comprehensive review of computational methods for identifying LMIs. Specifically, we first summarized the available public databases for predicting animal and plant LMIs. Secondly, we comprehensively reviewed the computational methods for predicting LMIs and classified them into two categories, including network-based methods and sequence-based methods. Thirdly, we analyzed the standard evaluation methods and metrics used in LMI prediction. Finally, we pointed out some problems in the current study and discuss future research directions. Relevant databases and the latest advances in LMI prediction are summarized in a GitHub repository https://github.com/sheng-n/lncRNA-miRNA-interaction-methods , and we'll keep it updated.

Measuring the business value of generative AI

Generative artificial intelligence (GenAI) can deliver tangible and intangible values that can be calculated to decide which projects benefit from GenAI and which do not. This paper is intended to be a guide for businesses just starting to build traction for their ideas. The focus is on evaluating and leveraging GenAI’s potential to innovate faster and compete effectively in a rapidly evolving digital economy. The paper specifies the many ways GenAI can have an impact on a business and considers how to measure that impact. It starts with standard business metrics (revenue, profit, customer satisfaction, etc.) and then turns to the more esoteric task of measuring the impact on creativity, inspiration and innovation, followed by business disruption and process metrics. It finishes with a look at improving process improvement.

A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder

Denoising is one of the most important preprocesses in image processing. Noises in images can prevent extracting some important information stored in images. Therefore, before some implementations such as image classification, segmentation, etc., image denoising is a necessity to obtain good results. The purpose of this study is to compare the deep learning techniques and traditional techniques on denoising facial images considering two different types of noise (Gaussian and Salt&amp;amp;Pepper). Gaussian, Median, and Mean filters have been specified as traditional methods. For deep learning methods, deep convolutional denoising autoencoders (CDAE) structured on three different optimizers have been proposed. Both accuracy metrics and computational times have been considered to evaluate the denoising performance of proposed autoencoders, and traditional methods. The utilized standard evaluation metrics are the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). It has been observed that overall, while the traditional methods gave results in shorter times in terms of computation times, the autoencoders performed better concerning the evaluation metrics. The CDAE based on the Adam optimizer has been shown the best results in terms of PSNR and SSIM metrics on removing both types of noise.

InFocus: Amplifying Critical Feature Influence on Non-Intrusive Load Monitoring Through Self-Attention Mechanisms

Non-intrusive load monitoring (NILM) enables extracting individual appliances’ power consumption data from an aggregated power signal in a cost-effective way. The extracted appliance-level power data can greatly facilitate tasks such as malfunction diagnosis and load forecasting, which are of significant importance for efficient energy use. Various neural networks including convolutional neural network (CNN), recurrent neural network (RNN), and transformer (self-attention-based neural network) are employed in the design of NILM solutions since 2015. In particular, CNN is revealed to extract certain critical features such as power level and typical usage duration, thus, achieving superior performance. However, the global features especially the dependency correlations between different positions in a sequence cannot be properly acquired. Accordingly, we devise a novel model incorporating an added attention layer to overcome this limitation. The added self-attention mechanism can automatically assign attention scores/weights to different features outputted by convolutional layers, which amplifies the positive influence of critical knowledge while realizing global reference. Moreover, this model can explicitly extract the appliance’s multi-state information, which endows the model with more interpretability. We further improve our model by substituting the added self-attention mechanism with a lightweight one, which decreases the number of model parameters while maintaining the decomposing accuracy. Experimental results over two real-world datasets, REDD and UK-DALE, demonstrate that our models outperform the state-of-the-art, achieving 6.5%-52% improvement on average for three standard evaluation metrics. Moreover, we offer an extracted NILM solution system architecture incorporating neural networks, aiming to establish a framework to support future research.

Abstract 138: Treatment Initiation During The First Month Following Hypertension Diagnosis Improves Blood Pressure Control Up To 30 Months

Background: Adults with hypertension (HTN) have fewer cardiovascular events if controlled within the first 6 months after diagnosis, during which time they are excluded from many HTN control metrics. We compared BP control rates (BP &lt;140/90) from 6 to 42 months in adults with HTN who did or did not have treatment initiated during the first month following diagnosis, irrespective of subsequent treatment initiation or intensification (TI). Methods: A patient cohort from 5 diverse healthcare organizations, engaged in a quality improvement program, was identified with previously undiagnosed and untreated HTN. TI and BP control were assessed by measuring the period prevalence of visit BP control and TI on uncontrolled visits, then compared with the time since diagnosis (TSD) up to 42 months. Groups were stratified by treatment initiation status during the first month. Results: A prospective cohort of 14,328 newly diagnosed and untreated persons with HTN was identified, mean age 56.2±14.7 years. Patients with TI during the first month had higher rates of BP control at 6 (56.2% vs 46.2%, p&lt;0.001) through 30 months (65.9% vs 61.4%, p&lt;0.001) than those initially untreated. Patients initiated during the first month were less likely to receive additional intensifications (OR 0.68, 95% CI [0.63-0.74]). Conclusion: TI in the first month after diagnosis results in better BP control at 6 months, which has prognostic importance and coincides with the time when these individuals impact standard HTN control metrics. Of concern, TI during the first month after diagnosis was insufficient to achieve control rates ≥70% and lowered the probability of subsequent TI.

Update on the evaluation and management of dysphagia in sporadic inclusion body myositis.

Dysphagia is a common symptom of sporadic inclusion body myositis (IBM), affecting disease trajectory and patient quality-of-life. Despite this, it is considerably understudied. The purpose of this review is to summarize current evidence related to the evaluation and management of dysphagia in IBM. We highlight a patient case involving a multidisciplinary management approach, and we encourage continued exploration of exercises for delaying progression and improving impairments in patients with IBM and dysphagia. Recent investigations confirm that dysphagia in IBM is a debilitating and complex symptom that warrants timely evaluation and management. Further, they highlight the lack of validation of standardized swallowing-related metrics specifically for IBM and the limited evidence supporting a consensus of management approaches. Small scale research and clinical anecdotal data support a multidisciplinary and multipronged patient-centered approach, including rehabilitative exercise protocols, for dysphagia management in IBM. A paucity exists in the literature to effectively guide clinical decision-making for patients with IBM and dysphagia. Given this, it is our belief that a careful multidisciplinary and multipronged patient-centered approach is critical for dysphagia management in IBM. Prospective, longitudinal research on the underlying mechanisms of swallowing dysfunction using advanced and validated swallowing-related outcome measures is urgently needed.