Correct Rate Research Articles

Hypertension management: A comparative analysis of garlic, hibiscus, hawthorn, and olive leaf and a survey

Hypertension, a prevalent cardiovascular condition characterized by elevated blood pressure, poses a significant global health challenge. This paper reviews the potential of garlic, hibiscus, hawthorn, and olive leaf in reducing hypertension and improving one’s health. A survey was conducted among first year pharmacy students. The survey included 5 knowledge-based and 5 opinion-based questions, focusing on the four herbal remedies. The knowledge-based questions showed a correct answer rate of 61.1%, with the statement that garlic is generally safe to include in one’s diet, but excessive consumption can lead to adverse effects garnering a 91.1% correct response rate. 58.3% of the respondents failed to know the simple definition of hypertension. Opinion-based questions revealed a positive attitude towards herbal remedies with an average of 81% showing agreement on the potential benefits. Nearly 83% believed in consulting health professionals before integrating olive leaf extract, and about 89% agreed that garlic should be used as an approach to manage hypertension.

Anti-gingivitis natural products: Aloe vera gel, tea tree oil mouthwash, turmeric gel, myrrh extract/mouthwash

Gingivitis is an inflammatory gum disease which occurs around the cervical portion of the gum and is classified by its clinical appearance. Gingivitis is very common, and anyone can be affected including but not limited to poor oral care habits. This paper reviews the potential of Aloe Vera, Tea Tree Oil Mouthwash, Turmeric Gel, and Myrrh Extract/Mouthwash. The study also assessed the knowledge and opinions of first-year pharmacy students. The survey part of the study included 5 knowledge-based and 5 opinion-based questions, focusing on the four herbal remedies. The knowledge-based questions showed a 65.7% correct response rate, with the question on turmeric receiving the highest accuracy (94.3%), while the question surveying the participants’ knowledge about the source of information for herbal products received the lowest correct answer rate at 31.4%. Opinion-based questions revealed a positive attitude towards herbal remedies, with about 67.6% to 94.3% strongly agreeing, or agreeing with the potential benefits of the products. The highest agreement rate at 94.3% was obtained for the statement that myrrh can help reduce inflammation in the gums and inhibit the growth of bacteria, thus assisting in the management of gingivitis. Disagreement of opinions was minimal, ranging from 5.7% to 32.3%, indicating a widespread inclination towards positive agreement.

Adult Individuals Applicable to the Family Health Center Knowledge and Awareness About Osteoporosis

Abstract Background The aim of this study is to determine the knowledge and awareness of adult individuals applying to the Family Health Center (FHC) about osteoporosis. Methods A descriptive design study was conducted at a FHC in Turkey/Istanbul between January and May 2022. The sample of the research consisted of 320 adults selected by purposeful sampling method. Ethical approval and necessary permissions were obtained from the the conduct of the study. Data were collected through a socio-demographic characteristics form, a 20-item single-subscale Osteoporosis Knowledge Assessment Questionnaire (OKAQ), and a 27-item Osteoporosis Awareness Scale (OAS). Descriptive statistics, normality tests, analysis of variance, Kruskal-Wallis analysis of variance, t-test, and Mann-Whitney U test were used for data analysis. Statistical significance was accepted at p < 0.05. Results T The average age of adults is 60.47±13.17 and 55.9% are female and 44.1% are male. 34.7% had completed middle school. Participants’ highest correct response rate in the osteoporosis knowledge assessment questionnaire was for the item ‘Osteoporosis increases the risk of bone fracture’ (81.6%), while the lowest correct response rate was for the item ‘Osteoporosis generally causes symptoms (such as pain) before fractures occur’ (26.3%). According to the Osteoporosis Awareness Scale, adults’ awareness levels were above average. They scored the highest in the preventive behaviors subscale and the lowest in the risk factors subscale. Adults with university-level education scored significantly higher in all subscales of the OAS compared to other education levels (p < 0.05). Individuals with knowledge about osteoporosis and those who consumed milk regularly scored significantly higher in all subscales of the OAS (p < 0.05). Conclusions Although adults’ awareness of osteoporosis is above average, their correct knowledge about osteoporosis is inadequate. Key messages • An individual’s education level affects osteoporosis knowledge and awareness. • It is recommended to plan educational initiatives to increase osteoporosis knowledge and awareness.

Increased feelings of nervousness among older Europeans post-pandemic

Abstract Background Feelings of nervousness are typically associated with situational factors and are temporary, less intense, and manageable with self-care. Study Aim This study examines which situational conditions are significant for feeling nervous among European older adults during the second wave of COVID-19. Methods Using data from Wave 9, COVID-19 Survey 2 (Summer, 2021) of the cross-national panel dataset the Survey of Health, Aging, and Retirement in Europe (SHARE), this study utilizes a sample of 49,253 adults over 50 years old from 27 countries in Europe. Analysis: Binary logistic regression indicates all variables are significant predictors of feeling nervous: Gender [Wald= 498.7, p = 0.001 (<0.05)]; chronic disease [Wald= 1635.002 p = 0.001 (<0.05)], employment [Wald= 164.544, p = 0.001 (<0.05)]; home care [Wald= 138.4, p = 0.001 (<0.05)], technology [Wald= 20.66, p = 0.001 (<0.05)]; social support [Wald= 106.66, p = 0.001 (<0.05)], and financial support [Wald=35.5, p = 0.001 (<0.05)] [Chi-Square=3216.401, df = 6 and p = 0.001 (p < 0.05)]. Findings The odds ratio (OR) for gender is .627 (95% CI .601- .653), for chronic disease, is 2.659 (95% CI: 1.716 - 4.119) and employment is .675 (95% CI: .635-.717) and homecare is (OR)3.47(95% CI: 2.83-4.28); OR for social support is 855(95% CI:.830-.881), financial support 1.779 (95% CI: 1.47-2.15), and technology (OR).956 (95% CI: 938-.975). The model correctly predicted 94% of cases without feeling nervous and 16% of cases where people did feel nervous, giving an overall percentage correct prediction rate of 68%. Implications for Policy and Practice: Knowledge from this study can inform the introduction of public health interventions for older adults targeted toward specific situational circumstances that reduce nervousness. Key messages • While feelings of nervousness can be situational, the impact on mental health can go beyond the contextual environment. • Public health policies can support targeted interventions aimed at developing the coping skills necessary to better respond in times of crisis.

Performance Assessment of GPT 4.0 on the Japanese Medical Licensing Examination.

To evaluate the accuracy and parsing ability of GPT 4.0 for Japanese medical practitioner qualification examinations in a multidimensional way to investigate its response accuracy and comprehensiveness to medical knowledge. We evaluated the performance of the GPT 4.0 on Japanese Medical Licensing Examination (JMLE) questions (2021-2023). Questions are categorized by difficulty and type, with distinctions between general and clinical parts, as well as between single-choice (MCQ1) and multiple-choice (MCQ2) questions. Difficulty levels were determined on the basis of correct rates provided by the JMLE Preparatory School. The accuracy and quality of the GPT 4.0 responses were analyzed via an improved Global Qualily Scale (GQS) scores, considering both the chosen options and the accompanying analysis. Descriptive statistics and Pearson Chi-square tests were used to examine performance across exam years, question difficulty, type, and choice. GPT 4.0 ability was evaluated via the GQS, with comparisons made via the Mann-Whitney U or Kruskal-Wallis test. The correct response rate and parsing ability of the GPT4.0 to the JMLE questions reached the qualification level (80.4%). In terms of the accuracy of the GPT4.0 response to the JMLE, we found significant differences in accuracy across both difficulty levels and option types. According to the GQS scores for the GPT 4.0 responses to all the JMLE questions, the performance of the questionnaire varied according to year and choice type. GTP4.0 performs well in providing basic support in medical education and medical research, but it also needs to input a large amount of medical-related data to train its model and improve the accuracy of its medical knowledge output. Further integration of ChatGPT with the medical field could open new opportunities for medicine.

Authenticating the Geographical Origin of Jingbai Pear in Northern China by Multiple Stable Isotope and Elemental Analysis

The Jingbai pear is one of the best pear species in China with high quality and nutrition values which are closely linked to its geographical origin. With the purpose of discriminating the PGI Mentougou Jingbai pear from three other producing regions, the stable isotope ratios and elemental profiles of the pears (n = 52) and the corresponding soils and groundwater were determined using isotope ratio mass spectrometry (IRMS) and inductively coupled plasma mass spectrometry (ICP-MS), respectively. The results revealed that δ15N, δ18OJ, and Li were significantly different (p < 0.05) in samples from different regions, which indicated their potential to be used in the geographical origin classification of the Jingbai pear. The nitrogen isotopic values of the pear pulp were positively correlated with the δ15N value and nitrogen content of the corresponding soils, whilst the B, Na, K, Cr, and Cd contents of the pear pulps were positively correlated with their corresponding soils. Orthogonal partial least squares discriminant analysis (OPLS-DA) was performed in combination with analysis of the stable isotopes and elemental profiles, making it possible to distinguish the cultivation regions from each other with a high prediction accuracy (a correct classification rate of 92.3%). The results of this study highlight the potential of stable isotope ratios and elemental profiles to trace the geographical origin of pears at a small spatial scale.

A Multi-Isotopic Chemometric Approach for Tracing Hazelnut Origins

High-value products, such as hazelnuts, are particularly vulnerable to fraud due to their price dependence on geographical origin. Guaranteeing hazelnuts’ authenticity is essential for consumer trust and safety. Stable isotope analysis has become a reference method for origin authentication as it is reliable, robust, and easily transferable across laboratories. However, multiple isotopic markers coupled with chemometric techniques are often needed to authenticate food provenance accurately. In this study, we focused on assessing the potential of bulk δ18O, along with δ2H and δ13C of the main fatty acids, as hazelnut-origin authenticity markers. PLS-DA classification models were developed to differentiate samples (n = 207) according to their region of origin. This multi-isotopic approach provided promising external validation results, achieving a 94% global correct classification rate in discriminating hazelnuts from regions with distinct geographical and environmental conditions. This study lays the groundwork for further model development and evaluation across additional production areas and harvest years.

Hemoglobinopathy Awareness Among Middle and High School Students In Karataş, Türkiye

Objective:Hemoglobinopathies are complex and inherited genetic disorders with no definitive cure; however, preventive measures such as genetic counseling and premarital screening may dramatically reduce their prevalence. Therefore, having sufficient information about the mentioned preventive measures is crucial. Methods:All students attending middle and high schools were included without a sample selection. A survey form was administered to students in order to evaluate their knowledge level and attitudes regarding hemoglobinopathies. The results were evaluated using Chi-square and logistic regression analysis with 95% confidence interval (CI). Results:The total number of students agreeing to participate in the study was 877. The mean age of the participants was 14.8 ± 1.5 years; 50.5% of them were male.Of the participating students, 40.3% were previously informed about hemoglobinopathies.The lowest correct response rate among questions in terms of students’ knowledge about hemoglobinopathies was observed in the question that asked the transmission route of the disorders with 13.7%.In the multivariate analysis, maternal education level (OR=1.417; 95% CI) and knowing an affected patient (OR=4.215; 95% CI) were associated with a greater likelihood of being previously informed. Conclusions:Our study demonstrated that students had insufficient knowledge on hemoglobinopathies in a region where the disorders are prevalent. Organizing and sustaining educational activities related to hemoglobinopathies - particularly genetic basics of the disorders - to students, who are the parents of the future, may be useful in combating hemoglobinopathies. Keywords:Hemoglobinopathies, Awareness, School Health, Middle School Students, High School Students

Beyond Words: Enhancing Clinical Guideline Comprehension with Icons

Abstract Background The GRADE (Grading Recommendations, Assessment, Development, and Evaluations) framework is widely applied in clinical guidelines to facilitate transparent evidence evaluation. While developing Infectious Diseases Society of America (IDSA) guidelines on the management of patients with COVID-19, panel members suggested developing and implementing a visual aid to enable quicker identification of key information by providers at bedside seeking guidance. Methods We conducted a mixed-method study evaluating the usability of a newly designed infographic/icon using a survey and focus groups. The survey incorporated a simulated COVID-19 IDSA guideline with and without the icon, followed by comprehension questions. Focus group discussions provided qualitative feedback on the GRADE methodology and icon usability. Results The survey was returned by 289 healthcare providers. There was no statistical difference in the correct response rates between icon-aided and non-icon-aided guideline questions (McNemar’s Chi-squared test, p>0.1 for both questions). Interactions with the icon notably increased the time taken and number of clicks required to respond to the first question (Wilcoxon signed-rank test, p<0.01). In contrast, response time didn’t differ between versions for the second question (p=0.38). Most subjects (85%) indicated that the icon improved the readability of the guidelines. A focus group follow-up suggested alternative designs for the icon. Conclusion The study highlights the promise of iconography in clinical guidelines, although the specific icons tested did not measurably improve usability metrics. Future research should focus on icon design and testing within a formal usability framework, considering the impact of GRADE language on user experience.

Generative adversarial networks for whispered to voiced speech conversion: a comparative study

AbstractGenerative Adversarial Networks (GANs) have demonstrated promising results as end-to-end models for whispered to voiced speech conversion. Leveraging non-autoregressive systems like GANs capable of performing conditional waveform generation eliminates the need for separate models to estimate voiced speech features, and leads to faster inference compared to autoregressive methods. This study aims to identify the optimal GAN architecture for the whispered to voiced speech conversion task by comparing six state-of-the-art models. Furthermore, we present a method for evaluating the preservation of speaker identity and local accent, using embeddings obtained from speaker- and language identification systems. Our experimental results show that building the speech conversion system based on the HiFi-GAN architecture yields the best objective evaluation scores, outperforming the baseline by $$\sim$$ ∼ 9% relative using frequency-weighted Signal-to-Noise Ratio and Log Likelihood Ratio, as well as by $$\sim$$ ∼ 29% relative using Root Mean Squared Error. In subjective tests, HiFi-GAN yielded a mean opinion score of 2.9, significantly outperforming the baseline with a score of 1.4. Furthermore, HiFi-GAN enhanced ASR performance and preserved speaker identity and accent, with correct language detection rates of up to $$\sim$$ ∼ 98%.

Intelligent Verification Tool for Surgical Information of Ophthalmic Patients-A Study Based on Artificial Intelligence Technology.

With the development of day surgery, the characteristics of "short, frequent and fast" ophthalmic surgery are becoming more prominent. However, nurses are not efficient in verifying patients' surgical information, and problems such as patient privacy leakage are becoming more prominent. To improve the situation, we developed a new augmented reality (AR)-based tool for visual recognition and artificial intelligent (AI) interpretation of the pattern and location of patient surgical skin markings for the verification of the correct surgical site and procedure. The tool can also display a variety of other verbally requested patient information. The purpose of this proposal is to evaluate its feasibility of use by surgical nurses in a real clinical setting. We developed a tool with image recognition technologies to interpretation patient surgical skin markings and match the information obtained with the patients surgical records, thus, verify the patient's surgical information. Verification includes the proper surgical site and type of procedure to be performed. Nurses can interact with the device through its speech recognition capabilities and the device provides them with a variety of other requested patient information via a heads-up display. Three hundred patients in an outpatient ophthalmology clinic were divided into an AR intelligent verification experimental group and a manual verification control group. The accuracy of information verification, work time consumption, and economic cost data were compared between the 2 groups to evaluate the effectiveness of the AR Surgical Information Intelligent Verification Tool in clinical patient surgical information verification. There was no statistically difference in the correct rates of patient surgical information review between the experimental group (95.33%) and the control group (98.67%) (χ2 = 2.934, P = 0.087). The median time for information verification was 10.00 (10.00, 11.00) seconds in the experimental group and 21.00 (20.00, 24.00) seconds in the control group, a statistically difference (Z = 0.000, P < 0.001). The experimental group saved 11 seconds per patient per review compared with the control group. Considering 10,531 surgeries in 2023, printing 1 page of surgical information per 9 patients and requiring 4 copies, 4680 pages of printing paper could be saved. The AR Surgical Information Intelligent Verification Tool has advantages in assisting medical staff in patient surgical information verification, improving nursing efficiency, preventing surgical mark errors or nonstandardization, protecting patient privacy, and saving costs. It has certain research and application value in the scenario of patient surgical information verification in ophthalmic day ward.

I-vector and variability compensation techniques for mobile phone recognition

Mobile phone recognition consists of trying to identify the mobile phone brand or model, which is very important in forensic analysis. In this paper, we exploit the audio recordings to realize this task. Audio data conveys many information, one of them is related to the source of records, i.e., is a specific audio was captured by a given mobile phone. In order to separate between this particular information and the rest of audio content, we introduce the I-vector technique. Additionally, Linear Discriminant Analysis (LDA) and Within-Class Covariance Normalization (WCCN) methods were used in different ways to compensate the speaker and channel variability. We evaluate the performance of our proposed system by correct rate recognition based on Gaussian Probabilistic Linear Discriminant Analysis (GPLDA) and Cosine Similarity Score (CSS) methods. To train the mobile phone models and do tests, two datasets were prepared: the public MOBIPHONE database and a novel local database. Experiments done with different configurations demonstrate the effectiveness of I-vectors models with the combination of LDA – WCCN compensation techniques and CSS as a similarity measurement method. A correct recognition rates of 97.23 %, 98.91 %, and 99.45 % were obtained for MOBIPHONE, the local Same Environment (SE) and Different Environment (DE) databases, respectively.

Assessing acute effects of two motor-cognitive training modalities on cognitive functions, postural control, and gait stability in older adults: a randomized crossover study.

The process of aging often accompanies a decline in cognitive function, postural control, and gait stability, consequently increasing the susceptibility to falls among older individuals. In response to these challenges, motor-cognitive training has emerged as a potential intervention to mitigate age-related declines. This study aims to assess the acute effects of two distinct motor-cognitive training modalities, treadmill dual-task training (TMDT) and interactive motor-cognitive training (IMCT), on cognitive function, postural control, walking ability, and dual-task performance in the elderly population. In this randomized crossover study, 35 healthy elderly individuals (aged 60-75) participated in three acute training sessions involving TMDT, IMCT, and a control reading condition. Assessments of executive function, postural control, gait performance, and cognitive accuracy were conducted both before and after each session. Both TMDT and IMCT improved executive functions. Notably, IMCT resulted in a significant enhancement in correct response rates and a reduction in reaction times in the Stroop task (p < 0.05) compared to TMDT and the control condition. IMCT also led to an increase in dual-task gait speed (p < 0.001) and showed a trend towards improved cognitive accuracy (p = 0.07). Conversely, TMDT increased postural sway with eyes open (p = 0.013), indicating a potential detriment to postural control. The findings suggest that IMCT holds greater immediate efficacy in enhancing cognitive function and gait stability among older adults compared to TMDT, with a lesser adverse impact on postural control. This underscores the potential of IMCT as a preferred approach for mitigating fall risk and enhancing both cognitive and physical functions in the elderly population.

Performance of ChatGPT-4o in the Adult Clinical Cardiology Self-Assessment Program

Abstract Objective This study evaluates the performance of OpenAI's latest large language model, ChatGPT-4o, on the Adult Clinical Cardiology Self-Assessment Program (ACCSAP). Methods ChatGPT-4o was tested on 639 ACCSAP questions, excluding 45 questions containing video clips, resulting in 594 questions for analysis. The questions included a mix of text-based and static image-based (ECG, angiogram, CT scan, echocardiogram) formats. The model was allowed one attempt per question. Further evaluation of image-only questions was performed on 25 questions from the database. Results ChatGPT-4o correctly answered 69.2% (411/594) of the questions. The performance was higher for text-only questions (73.9%) compared to those requiring image interpretation (55.3%, p &amp;lt; 0.001). The model performed worse on questions involving ECGs, with a correct rate of 56.5% compared to 73.3% for non-ECG questions (p &amp;lt; 0.001). Despite its capability to interpret medical images in the context of a text-based question, the model's accuracy varied, demonstrating strengths and notable gaps in diagnostic accuracy.It lacked accuracy in reading images (ECGs, echocardiography, angiograms) with no context. Conclusions ChatGPT-4o performed moderately well on ACCSAP questions. However, the model's performance remains inconsistent, especially in interpreting ECGs. These findings highlight the potential and current limitations of using large language models in medical education and clinical decision-making.

A Lightweight GCT-EEGNet for EEG-Based Individual Recognition Under Diverse Brain Conditions

A robust biometric system is essential to mitigate various security threats. Electroencephalography (EEG) brain signals present a promising alternative to other biometric traits due to their sensitivity, non-duplicability, resistance to theft, and individual-specific dynamics. However, existing EEG-based biometric systems employ deep neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which face challenges such as high parameter complexity, limiting their practical application. Additionally, their ability to generalize across a large number of subjects remains unclear. Moreover, they have been validated on datasets collected in controlled environments, which do not accurately reflect real-world scenarios involving diverse brain conditions. To overcome these challenges, we propose a lightweight neural network model, GCT–EEGNet, which is based on the design ideas of a CNN model and incorporates an attention mechanism to pay attention to the appropriate frequency bands for extracting discriminative features relevant to the identity of a subject despite diverse brain conditions. First, a raw EEG signal is decomposed into frequency bands and then passed to GCT–EEGNet for feature extraction, which utilizes a gated channel transformation (GCT) layer to selectively emphasize informative features from the relevant frequency bands. The extracted features were used for subject recognition through a cosine similarity metric that measured the similarity between feature vectors of different EEG trials to identify individuals. The proposed method was evaluated on a large dataset comprising 263 subjects. The experimental results demonstrated that the method achieved a correct recognition rate (CRR) of 99.23% and an equal error rate (EER) of 0.0014, corroborating its robustness against different brain conditions. The proposed model maintains low parameter complexity while keeping the expressiveness of representations, even with unseen subjects.

Fruit Distribution Density Estimation in YOLO-Detected Strawberry Images: A Kernel Density and Nearest Neighbor Analysis Approach

Precise information on strawberry fruit distribution is of significant importance for optimizing planting density and formulating harvesting strategies. This study applied a combined analysis of kernel density estimation and nearest neighbor techniques to estimate fruit distribution density from YOLOdetected strawberry images. Initially, an improved yolov8n strawberry object detection model was employed to obtain the coordinates of the fruit centers in the images. The results indicated that the improved model achieved an accuracy of 94.7% with an mAP@0.5~0.95 of 87.3%. The relative error between the predicted and annotated coordinates ranged from 0.002 to 0.02, demonstrating high consistency between the model predictions and the annotated results. Subsequently, based on the strawberry center coordinates, the kernel density estimation algorithm was used to estimate the distribution density in the strawberry images. The results showed that with a bandwidth of 200, the kernel density estimation accurately reflected the actual strawberry density distribution, ensuring that all center points in high-density regions were consistently identified and delineated. Finally, to refine the strawberry distribution information, a comprehensive method based on nearest neighbor analysis was adopted, achieving target area segmentation and regional density estimation in the strawberry images. Experimental results demonstrated that when the distance threshold ϵ was set to 600 pixels, the correct grouping rate exceeded 94%, and the regional density estimation results indicated a significant positive correlation between the number of fruits and regional density. This study provides scientific evidence for optimizing strawberry planting density and formulating harvesting sequences, contributing to improved yield, harvesting efficiency, and reduced fruit damage. In future research, this study will further explore dynamic models that link fruit distribution density, planting density, and fruit growth status.

A traffic incident detection method of highway based on spatial-temporal characteristics

In this study, we propose a method for detecting traffic incidents by leveraging tensor decomposition in conjunction with spatial-temporal constraints. The method comprises three main stages. Firstly, we develop an initialization and noise reduction procedure for the traffic data pre-processing model, which includes Tucker decomposition and truncating higher-order Singular Value Decomposition (SVD). Subsequently, we construct spatial and temporal coefficient matrices based on the Pearson test and introduce a Laplace penalty term to quantify the disparity between predicted and actual data. Next, we validate the method using traffic loop detector data and incident records from I90 in Seattle, USA, in 2015, comparing its performance with seven other traffic prediction methods. The results demonstrate the superior prediction accuracy, correct detection rate, and low false alarm rate of our traffic incident detection method. Specifically, the mean square error of speed prediction is 5.22 km / h , the average relative error is 6.88 % , and the detection rate reaches 98.92 % . Our method effectively evaluates the spatial-temporal characteristics of traffic data, enabling accurate prediction and detection of traffic incidents. Its technical applicability holds promise for enhancing the capacity and efficiency of future traffic control systems.

Emergency imaging protocols for pregnant patients: a multi-institutional and multi- specialty comparison of physician education.

Previous studies have demonstrated that radiologists and other providers perceive maging to be higher than they actually are. Thus, pregnant patients were less likely to receive ionizing radiation procedures. While it is imperative to minimize fetal radiation exposure, clinicians must remember that diagnostic studies should not be avoided due to fear of radiation, particularly if the imaging study can significantly impact patient care. Although guidelines do exist regarding how best to image pregnant patients, many providers are unaware of these guidelines and thus lack confidence when making imaging decisions for pregnant patients. This study aimed to gather information about current education, confidence in, and knowledge about emergency imaging of pregnant women among radiology, emergency medicine, and OB/GYN providers. We created and distributed an anonymous survey to radiology, emergency medicine, and OB/GYN providers to evaluate their knowledge and confidence in imaging pregnant patients in the emergent setting. This study included a questionnaire with the intent of knowing the correct answers among physicians primarily across the United States (along with some international participation). We conducted subgroup analyses, comparing variables by specialty, radiology subspecialty, and training levels. Based on the survey results, we subsequently developed educational training videos. 108 radiologists, of which 32 self-identified as emergency radiologists, ten emergency medicine providers and six OB/GYN clinicians completed the survey. The overall correct response rate was 68.5%, though performance across questions was highly variable. Within our 18-question survey, four questions had a correct response rate under 50%, while five questions had correct response rates over 90%. Most responding physicians identified themselves as either "fairly" (58/124, 47%) or "very" (51/124, 41%) confident. Amongst specialties, there were differences in performance concerning the knowledge assessment (p = 0.049), with the strongest performance from radiologists. There were no differences in knowledge by training level (p = 0.4), though confidence levels differed significantly between attending physicians and trainees (p < 0.001). This study highlights deficiencies in knowledge to support appropriate decision-making surrounding the imaging of pregnant patients. Our results indicate the need for improved physician education and dissemination of standardized clinical guidelines.

Effectiveness of Generative AI for Post-Earthquake Damage Assessment

After an earthquake, rapid assessment of building damage is crucial for emergency response, reconstruction planning, and public safety. This study evaluates the performance of various Generative Artificial Intelligence (GAI) models in analyzing post-earthquake images to classify structural damage according to the EMS-98 scale, ranging from minor damage to total destruction. Correct classification rates for masonry buildings varied from 28.6% to 64.3%, with mean damage grade errors between 0.50 and 0.79, while for reinforced concrete buildings, rates ranged from 37.5% to 75.0%, with errors between 0.50 and 0.88. Fine-tuning these models could substantially improve accuracy. The practical implications are significant: integrating accurate GAI models into disaster response protocols can drastically reduce the time and resources required for damage assessment compared to traditional methods. This acceleration enables emergency services to make faster, data-driven decisions, optimize resource allocation, and potentially save lives. Furthermore, the widespread adoption of GAI models can enhance resilience planning by providing valuable data for future infrastructure improvements. The results of this work demonstrate the promise of GAI models for rapid, automated, and precise damage evaluation, underscoring their potential as invaluable tools for engineers, policymakers, and emergency responders in post-earthquake scenarios.

Radiation information, protection and awareness of healthcare professionals in terms of occupational health and safety: a two–center survey study

Abstract The objective of this study was to assess the level of knowledge, protection and awareness of radiation among healthcare professionals. A total of 413 healthcare professionals from two major training and research hospitals in Istanbul took part in the study. The sample consisted of 26.6% physicians and 73.4% other healthcare professionals. There was considerable variation in the knowledge of radiation content in imaging techniques, with correct response rates of 89.3% for Computed Tomography, 80.1% for digital x-ray, and 52.8% for Magnetic Resonance Imaging. While 69.5% of respondents provided correct answers to questions regarding radiation protection principles, 64.4% demonstrated a lack of sufficient knowledge in this area. It is noteworthy that 48.4% of respondents incorrectly identified the medical imaging technique to which the public is most exposed. It was concluded that further training on radiation-related issues should be provided at the university level and through in-service training for healthcare professionals.