Adjunctive Tool Research Articles

Abstract 14133: Use of Computed Tomography Coronary Angiography in Pre-Operative Risk Assessment in Non-Cardiac Surgeries

Introduction: Computed Tomography Coronary Angiography (CTCA) has emerged as a promising tool for detecting and evaluating coronary artery diseases (CAD). However, the role of CTCA for risk assessment tool for patients undergoing noncardiac surgeries is unknown. Methods: We searched databases like PubMed and Google Scholar to compile the literature including reviews, meta-analyses, scientific articles, and guidelines, describing the use of CTCA in the diagnosis and evaluation of cardiac risk assessment in noncardiac surgeries. Results: Based on our literature review we found that CTCA is a promising adjunctive tool for preoperative assessment in noncardiac surgeries to evaluate cardiac risk with high sensitivity and negative predictive value. VISION-CTA sub study showed that CTCA combined with Myocardial Perfusion Imaging (MPI) predicts major adverse cardiac events within 30 days of surgery with higher accuracy than either test alone. McCarthy KJ et al found an increase in the proportion of CTCAs performed from before 2018, 2018 to post 2018, (3%, 6.3%, and 26.3% respectively; p <0.001) and obstructive CAD diagnosis using CTCA (0%, 30%, and 51.4% respectively; p = 0.04). Hence supporting a shift to non-invasive CTCA for pre-operative assessment of liver transplant candidates. According to Ahn J et al, CTCA shows 100% sensitivity and 99.3% negative predictive value in predicting major adverse cardiac events and can be used to stratify intermediate risk noncardiac surgery patients. Conclusions: CTCA is a valuable risk assessment tool in patients undergoing noncardiac surgery because of its high sensitivity and negative predictive value. There is a need for further research and guidelines for using CTCA as a risk assessment tool in noncardiac surgeries.

Role of Hemodynamic Assessment and Limitations in Ankle-Brachial Pressure Index, Toe- Brachial Pressure Index to Predict Wound Healing After Revascularization.

Chronic limb-threatening ischemia (CLTI) represents one of the most severe forms of peripheral arterial disease implying impaired wound healing and tissue loss at the same time posing a significant impact on the quality of life of patients and a serious economic burden on healthcare systems around the world. A major challenge in the management of patients with CLTI is the validity and role of non-invasive hemodynamic parameters in assessing their clinical status before and after revascularization. Traditionally, the diagnosis of CLTI is routinely based on clinical symptoms and confirmed by measurements of non-invasive limb hemodynamics including ankle-brachial pressure index (ABPI) and toe-brachial pressure index (TBPI). However, whether these indices alone can provide definitive treatment or be used as adjunctive tool along with the implementation of novel techniques to help guide revascularization for CLI patients still remains unclear.

Measurable Residual Disease Monitoring in Lymphoma.

The utility of analyzing circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and disease in the bone marrow as an adjunctive tool in caring for hematologic cancer patients is expanding. This holds true for lymphoma where these biomarkers are being explored as a means of genotyping and quantifying disease. Regarding the latter, they can be used to monitor measurable residual disease (MRD) during and after treatment. This holds potential for aiding clinical decisions amidst treatment, detecting earlier relapse, and improving prognostication. Here, we review the evidence to support these applications in a variety of lymphoma subtypes. Numerous clinical trials across a variety of lymphomas have demonstrated value in MRD monitoring. MRD monitoring is often prognostic for progression free survival (PFS) and even overall survival (OS) at several time points in a disease course, particularly when utilizing serial measurements. With regards to tailoring treatment, there are a growing number of trials examining MRD-adaptive treatment strategies to intensify or de-escalate treatment to individualize care. Lastly, MRD monitoring has been utilized successfully in detecting earlier relapse when compared to more standard methods of clinical surveillance such as radiographic assessment. Although not routinely implemented into clinical practice, MRD monitoring in lymphoma is helping shape the future landscape of this disease by aiding in prognostication, guiding therapy, and detecting earlier relapse. Steps to standardize and further examine this technology prospectively are being taken to bring MRD monitoring to the forefront of the field.

Clinical features and outcomes of in-hospital cardiac arrest in code blue events: a retrospective observational study.

In-hospital cardiac arrest (IHCA) is a critical medical event with outcomes less researched compared to out-of-hospital cardiac arrest. This retrospective observational study aimed to investigate key aspects of IHCA epidemiology and prognosis in patients with Code Blue activation. This retrospective observational study enrolled patients with Code Blue events in our hospital between January 2010 and October 2019. Participant characteristics, including age and sex, and IHCA characteristics, including the time of cardiac arrest, witnessed event, bystander cardiopulmonary resuscitation (CPR), initial shockable rhythm, vital signs at 1 and 6 h before IHCA, survival to hospital discharge (SHD), and the cardiac arrest survival postresuscitation in-hospital (CASPRI) score were included in univariate and multivariate logistic regression analyses with SHD as the primary endpoint. From the 293 Code Blue events that were activated during the study period, 81 participants were enrolled. Overall, the SHD rate was 28.4%, the median CPR duration was 14 (interquartile range, 6-28) min, and the rate of initial shockable rhythm was 19.8%. There were significant intergroup differences between the SHD and non-SHD groups in the CPR duration, shockable rhythm, and CASPRI score on univariate logistic regression analysis. Multivariate logistic regression analysis showed that the CASPRI score was the most accurate predictive factor for SHD (OR = 0.98, p = 0.006). The CASPRI score is associated with SHD in patients with IHCA during Code Blue events. Therefore, the CASPRI score of IHCA patients potentially constitutes a simple, useful adjunctive tool for the management of post-cardiac arrest syndrome.

Evaluating Chatgpt As an Adjunct for Analyzing Challenging Case

Introduction Despite the rapid development of medicine, clinicians still faced with many challenging cases in practice. For challenging cases, most inexperienced doctors are unable to make a clear diagnosis in the first place, resulting in misdiagnosis and delayed disease. Recently, OpenAI released a new Chatbot model, called ChatGPT (Generation Pre training Converter), which is an artificial intelligence (AI) system that uses reinforcement learning model based on human feedback for training. There have been many research reports on the application of ChatGPT in the medical field, and it has been proven to have a wide range of clinical applications. In our study, we aimed to assess the accuracy and reliability of ChatGPT in diagnosing and treating challenging case. Methods We provided 2 challenging cases. Case 1 provided a medical history and auxiliary test results for ChatGPT, and then ChatGPT answered the possible diagnosis, diagnostic basis, further auxiliary examination and treatment. Case 2 provides negative symptoms and auxiliary test results for ChatGPT, which answers all possible diagnoses. Conduct a comprehensive evaluation and scoring of the results of ChatGPT responses by 5 senior physicians. A score of 10 is the perfect score. Results The analysis results of 2 challenging cases by ChatGPT were comprehensively evaluated by senior experienced professional doctors and scored 9 points. We found that ChatGPT's analysis of challenging case is comprehensive. Especially for the diagnosis and treatment of diseases, ChatGPT analyzes all possible diagnoses and provides accurate treatment plans through the provided medical history, auxiliary examination results, and treatment processes. ChatGPT can directly interpret the auxiliary examination results, such as high level of procalcitonin (PCT), abnormal kidney function, and with negative results can rule out certain diseases, such as ANA, ENA, and ANCA negative, ruling out the possibility of autoimmune diseases. ChatGPT can summarize all the diagnoses including rare diseases, based on the results of an auxiliary test, and explain the cause of the disease and the pathogenesis in detail. Conclusions Our study shows that ChatGPT can provide important reference value as an adjunct to clinicians in challenging case analysis. In particular, it can assist inexperienced doctors in remote areas and underdeveloped medical conditions to make clear diagnosis and treatment as soon as possible, avoid unnecessary referral to higher-level hospitals, reduce medical burden, and reduce waste of medical resources. In the medical field, ChatGPT needs more research to evaluate in clinical applications and become a better adjunct tool in clinical work.

Development and Validation of a Risk Prediction Tool for In-hospital Mortality After Thoracic Endovascular Repair in Patients with Blunt Thoracic Aortic Injury Using the Aortic Trauma Foundation Registry

The objective of our present effort was to use an international blunt thoracic aortic injury (BTAI) registry to create a prediction model identifying important preoperative and intraoperative factors associated with postoperative mortality, and to develop and validate a simple risk prediction tool that could assist with patient selection and risk stratification in this patient population. For the purpose of the present study, all patients undergoing thoracic endovascular aortic repair (TEVAR) for BTAI and registered in the Aortic Trauma Foundation (ATF) database from January 2016 as of June 2022 were identified. Patients undergoing medical management or open repair were excluded. The primary outcome was binary in-hospital all-cause mortality. Two predictive models were generated: a preoperative model (i.e. only including variables before TEVAR or intention-to-treat) and a full model (i.e. also including variables after TEVAR or per-protocol). Out of a total of 944 cases included in the ATF registry until June 2022, 448 underwent TEVAR and were included in the study population. TEVAR for BTAI was associated with an 8.5% in-hospital all-cause mortality in the ATF dataset. These study subjects were subsequently divided using 3:1 random sampling in a derivation cohort (336; 75.0%) and a validation cohort (112; 25.0%). The median age was 38years, and the majority of patients were male (350; 78%). A total of 38 variables were included in the final analysis. Of these, 17 variables were considered in the preoperative model, 9 variables were integrated in the full model, and 12 variables were excluded owing to either extremely low variance or strong correlation with other variables. The calibration graphs showed how both models from the ATF dataset tended to underestimate risk, mainly in intermediate-risk cases. The discriminative capacity was moderate in all models; the best performing model was the full model from the ATF dataset, as evident from both the Receiver Operating Characteristic curve (Area Under the Curve 0.84; 95% CI 0.74-0.91) and from the density graph. In this study, we developed and validated a contemporary risk prediction model, which incorporates several preoperative and postoperative variables and is strongly predictive of early mortality. While this model can reasonably predict in-hospital all-cause mortality, thereby assisting physicians with risk-stratification as well as inform patients and their caregivers, its intrinsic limitations must be taken into account and it should only be considered an adjunctive tool that may complement clinical judgment and shared decision-making.

Gabapentin, melatonin, and acepromazine combination prior to hospital visits decreased stress scores in aggressive and anxious dogs in a prospective clinical trial.

To evaluate sedative and behavioral effects of a client-administered preappointment protocol with PO gabapentin and melatonin and oral-transmucosal acepromazine (GMA protocol). 45 client-owned dogs between 1 and 12 years old that underwent standardize examinations between February and August 2021. In this clinical trial, dogs with a history of anxiety, fearfulness, and/or aggression during hospital visits were assessed and videotaped before (baseline) and after administration of the GMA protocol. For the second visit, owners administered PO gabapentin (20 to 25 mg/kg) in the evening prior to the next visit and PO gabapentin (20 to 25 mg/kg), PO melatonin (3 to 5 mg/dog), and oral-transmucosal acepromazine (0.05 mg/kg) 90 to 120 minutes prior to the second appointment. Examinations were performed, and behavioral stress and sedation levels were evaluated with semiquantitative rating scales. Randomized videos were analyzed, and a paired t test was used to compare stress and sedation scores between baseline and GMA. A Pearson correlation coefficient was used to evaluate the effect of age on the scores. Stress scores were significantly lower after the GMA protocol, and sedation scores were significantly higher when compared to baseline (21.84 vs 27.11 and 1.39 vs 0.68, respectively). A significant correlation between increasing age and lower stress scores post-GMA and higher sedation scores post-GMA were observed. Preappointment administration of the GMA protocol reduced signs of stress, fear, and fear-based aggression during hospital visits and provided sedation in this dog population. This protocol could represent an adjunct tool for veterinarians to improve quality of care and reduce animal-related injury.

3EDANFIS: Three Channel EEG-Based Depression Detection Technique with Hybrid Adaptive Neuro Fuzzy Inference System

Background: Depression is a mental disorder that often negatively impacts the actions and feelings of the affected person. No laboratory tests are available to detect and properly diagnose depression. Presently, the detection of depression is done based on standardized questionnaires like Diagnostic and Statistical Manual of Mental Disorders-fifth edition (DSM-V) and Hamilton Depression Rating Scale (HAM-D) which is subjective in nature. Objective: The purpose of the study is to propose a framework for more accurate detection of depression from EEG signals using only three channels, which makes the system portable as well as efficient. Methods: In this study, we propose a classification model using EEG signal with the help of Adaptive Neuro Fuzzy Inference System optimized by nature-inspired algorithm. The proposed model is efficient, accurate, and portable as the features are extracted from only three channels, namely, Fp1, Fp2, and Fz. The three Data Channel (3EDANFIS) Adaptive Neuro Fuzzy Inference System (ANFIS) for detection of depression as well as three variants of Hybrid ANFIS – Adaptive Neuro Fuzzy Inference System-Genetic Algorithm (ANFIS-GA), Adaptive Neuro Fuzzy Inference System- Particle Swam Optimization (ANFIS-PSO) and Adaptive Neuro Fuzzy Inference System- Firefly Algorithm (ANFIS-FA) has been analyzed in this study. The features extracted are delta, theta, alpha, and beta and their corresponding sub-bands delta1, delta2, theta1, theta2, alpha1, alpha2, beta1, and beta2. Genetic Algorithm (GA), Particle Swam Optimization (PSO), and Firefly Algorithm (FA) are all nature-inspired metaheuristic algorithms which are used to optimize ANFIS by adapting the premise and consequent parameters. Results: The analysis showed that the GA and FA perform equally well in optimizing ANFIS with the highest accuracy of 83.33 % using delta1 power as well as delta power. Overall accuracy of the ANFIS-GA is found to be higher than that of the ANFIS-PSO, ANFIS-FA, and ANFIS. It was also found that the sub-band classification accuracy is higher than that of the band itself for delta, theta, and alpha bands. In case of the ANFIS, ANFIS-GA, ANFIS-PSO, and ANFIS-FA, delta1 was found to be having higher accuracy than delta power, theta1 was found to be having higher accuracy than theta power, and both alpha1 and alpha2 showed higher accuracy than alpha power. Conclusion: The use of only three EEG channels for data recording makes our technique to be more feasible, portable, convenient, and faster and hence can act as an adjunct tool for psychiatrists in the future.

River Reach: Chicagoland, riverine reflections and settler harm-reduction poetry

Both case report and epic, ‘River Reach’ is an exploratory anamnesis that presents a metamodern embodied reflection of systems entanglement – exposing attunements to and longings for a place-based riverine kinship capable of addressing a national heritage of displacements, immigrations, and enslavement. To improve state compliance with the US Clean Water Act, Graue Mill dam on Salt Creek at Fullersburg Woods (Oak Brook, IL) will be removed in coming months for creek restoration and re-realignment, creating a point of collision and potential among pasts and futures. ‘River Reach’ is a hyperlocal example of a practical, approachable step toward decolonial poetry and community – examining the insularities and permeabilities of whiteness while encouraging personal, deliberate engagement in the preparatory work required for recalibrating national identity cooperatively. As a centering poem for settler harm reduction and an adjunctive tool for collective action, the piece invites participants into a mimetic rehabitation of relationship through nature. ‘River Reach’ may be performed collectively in collaboration with movement, audiovisual elements, and facilitated reader/audience participation.

Investigating pulse-echo sound speed estimation in breast ultrasound with deep learning

Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians in diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound speed to form diagnostic B-mode images. However, the components of breast tissue, such as glandular tissue, fat, and lesions, differ in sound speed. Given a constant sound speed assumption, these differences can degrade the quality of reconstructed images via phase aberration. Sound speed images can be a powerful tool for improving image quality and identifying diseases if properly estimated. To this end, we propose a supervised deep-learning approach for sound speed estimation from analytic ultrasound signals. We develop a large-scale simulated ultrasound dataset that generates representative breast tissue samples by modeling breast gland, skin, and lesions with varying echogenicity and sound speed. We adopt a fully convolutional neural network architecture trained on a simulated dataset to produce an estimated sound speed map. The simulated tissue is interrogated with a plane wave transmit sequence, and the complex-value reconstructed images are used as input for the convolutional network. The network is trained on the sound speed distribution map of the simulated data, and the trained model can estimate sound speed given reconstructed pulse-echo signals. We further incorporate thermal noise augmentation during training to enhance model robustness to artifacts found in real ultrasound data. To highlight the ability of our model to provide accurate sound speed estimations, we evaluate it on simulated, phantom, and in-vivo breast ultrasound data.

Active Matrix Metalloproteinase-8 (aMMP-8) Versus Total MMP-8 in Periodontal and Peri-Implant Disease Point-of-Care Diagnostics.

Active matrix metalloproteinase-8 (aMMP-8) is a promising biomarker candidate for the modern periodontal and peri-implant disease diagnostics utilizing the chairside/point-of-care oral fluid technologies. These rapid biomarker analysis technologies utilize gingival crevicular fluid (GCF), peri-implant sulcular fluid (PISF), or mouth rinse as the oral fluid matrices that can be collected patient-friendly and non-invasively without causing bacteremia. aMMP-8, but not total or latent proMMP-8, has been shown to be a relevant biomarker to be implemented to the latest 2017 classification system of periodontitis and peri-implantitis. Thus, aMMP-8 point-of-care-testing (POCT)-but not total or latent proMMP-8-can be conveniently used as an adjunctive and preventive diagnostic tool to identify and screen the developing and ongoing periodontal and peri-implant breakdown and disease as well as predict its episodic progression. Similarly, aMMP-8 POCT provides an important tool to monitor the treatment effect of these diseases, but also other diseases such as head and neck cancer, where it can identify and predict the rapid tissue destructive oral side-effects during and after the radiotherapy. Additionally, recent studies support aMMP-8 POCT benefitting the identification of periodontitis and diabetes as the escalating risk diseases for COVID-19 infection. Overall, aMMP-8 POCT has launched a new clinical field in oral medicine and dentistry, i.e., oral clinical chemistry.

Preoperative Thyroid-Stimulating Hormone Levels and Risk of Thyroid Cancer in Post-thyroidectomy Patients for Thyroid Nodules: A Study From a Tertiary Hospital in Western Saudi Arabia.

Background Although serum thyroid-stimulating hormone (TSH) is one of the basic investigations to assess thyroid nodules, its role in thyroid oncogenesis remains unclear. Previous literature has conflicting findings regarding TSH levels and the prediction of malignancy. This study aims to investigate the association between TSH levels and the risk of malignancy and advanced staging in patients who underwent thyroidectomy for nodular thyroid disease. Additionally, it aims to assess if higher TSH correlates with malignancy in Bethesda staging III, IV, and V. Methodology This retrospective cohort study was conducted among participants who underwent near-total/total thyroidectomy or hemithyroidectomy at King Abdulaziz Medical City between 2016 and 2021. Results A total of 378 cases were included, and 50.3% of the cases had malignant nodules in the surgical histopathology findings. The median TSH levels were higher in malignant nodules compared to benign ones (1.64 mIU/L versus 1.49 mIU/L; p < 0.001). Additionally, higher TSH levels were not associated with advanced staging or malignancy in patients with Bethesda stage III-V. Conclusions Higher TSH levels are associated with an increased risk of malignancy in patients with nodular thyroid disease. Using TSH levels as an adjunctive tool for identifying high-risk patients with thyroid nodules would aid in management planning.

ChatGPT performance in laryngology and head and neck surgery: a clinical case-series.

To study the performance of ChatGPT in the management of laryngology and head and neck (LHN) cases. History and clinical examination of patients consulting at the Otolaryngology-Head and Neck Surgery department were presented to ChatGPT, which was interrogated for differential diagnosis, management, and treatment. The ChatGPT performance was assessed by two blinded board-certified otolaryngologists using the following items of a composite score and the Ottawa Clinic Assessment Tool: differential diagnosis; additional examination; and treatment options. The complexity of clinical cases was evaluated with the Amsterdam Clinical Challenge Scale test. Forty clinical cases were submitted to ChatGPT, accounting for 14 (35%), 12 (30%), and 14 (35%) easy, moderate and difficult cases, respectively. ChatGPT indicated a significant higher number of additional examinations compared to practitioners (p = 0.001). There was a significant agreement between practitioners and ChatGPT for the indication of some common examinations (audiometry, ultrasonography, biopsy, gastrointestinal endoscopy or videofluoroscopy). ChatGPT never indicated some important additional examinations (PET-CT, voice quality assessment, or impedance-pH monitoring). ChatGPT reported highest performance in the proposition of the primary (90%) or the most plausible differential diagnoses (65%), and the therapeutic options (60-68%). The ChatGPT performance in the indication of additional examinations was lowest. ChatGPT is a promising adjunctive tool in LHN practice, providing extensive documentation about disease-related additional examinations, differential diagnoses, and treatments. The ChatGPT is more efficient in diagnosis and treatment, rather than in the selection of the most adequate additional examination.

Effect of Intralesional Steroid Injections Among Patients with Acquired Laryngotracheal Stenosis Undergoing Endoscopic Balloon Dilation Using Pulmonary Function Tests.

Objectives: Laryngotracheal stenosis (LTS) is characterized by an abnormal decrease in the upper airway diameter. The pulmonary function test (PFT) is an effective adjunctive diagnostic tool for upper airway obstruction. LTS can be managed with either open surgery or less invasive endoscopic approaches, among which endoscopic balloon dilation is the main method; this may include concurrent intralesional steroid injection (ILSI), which has the potential of improving the outcomes. However, the effectiveness of ILSI is unclear. We aimed to compare the improvement in PFT parameters among patients with acquired LTS following endoscopic balloon dilation who received and did not receive ILSIs. We also compared the recurrence times and rates between the 2 patient cohorts. Methods: We retrospectively collected data regarding pre- and postoperative PFTs, as well as inter-dilation interval records, obtained between June 2015 and April 2020. Results: We included 34 patients with acquired etiologies. The most common cause of stenosis was intubation (52.9%), followed by trauma (29.4%). Further, 52.9% of the patients received ILSIs. Symptom recurrence was reported in 23 (67.6%) cases, with no significant between-group difference -0.1389 [95% confidence interval (CI): -0.4483, 0.1705]. The mean (standard deviation) duration of the first reintervention was 8.62 (8.00) and 7.38 (3.20) months among patients who did and did not receive ILSIs, respectively (mean difference -1.23, P = .614, 95% CI -6.30, 3.84). Conclusion: Our findings indicated that PFT parameters improved following endoscopic balloon dilation, with forced expiratory volume in 1 second being significantly higher with concurrent ILSI. However, there was no between-method difference in the treatment effectiveness. Additionally, the restenosis recurrence rate was consistent with that reported in the literature.

Advantages and pitfalls in utilizing artificial intelligence for crafting medical examinations: a medical education pilot study with GPT-4

BackgroundThe task of writing multiple choice question examinations for medical students is complex, timely and requires significant efforts from clinical staff and faculty. Applying artificial intelligence algorithms in this field of medical education may be advisable.MethodsDuring March to April 2023, we utilized GPT-4, an OpenAI application, to write a 210 multi choice questions-MCQs examination based on an existing exam template and thoroughly investigated the output by specialist physicians who were blinded to the source of the questions. Algorithm mistakes and inaccuracies, as identified by specialists were classified as stemming from age, gender or geographical insensitivities.ResultsAfter inputting a detailed prompt, GPT-4 produced the test rapidly and effectively. Only 1 question (0.5%) was defined as false; 15% of questions necessitated revisions. Errors in the AI-generated questions included: the use of outdated or inaccurate terminology, age-sensitive inaccuracies, gender-sensitive inaccuracies, and geographically sensitive inaccuracies. Questions that were disqualified due to flawed methodology basis included elimination-based questions and questions that did not include elements of integrating knowledge with clinical reasoning.ConclusionGPT-4 can be used as an adjunctive tool in creating multi-choice question medical examinations yet rigorous inspection by specialist physicians remains pivotal.

Treatment of Complex Thoracic and Abdominal Trauma Patients: A Review of Literature and Negative Pressure Wound Therapy Treatment Options.

Significance: In trauma care, extensive surgical intervention may be required. Damage control surgery (DCS) is applicable to patients with life or limb-threatening conditions that are incapable of tolerating a traditional surgical approach. Recent Advances: The current resuscitation strategy for complex trauma patients includes limiting crystalloid fluids, balanced mass transfusion protocols, permissive hypotension, and damage control resuscitation. Recent technological advancements in surgical critical care have improved outcomes in these critically ill patients. Critical Issues: DCS, which is often required in patients with trauma injuries, is typically followed by surgical correction of the injury once the immediate patient survival procedures have been completed. However, DCS and the subsequent injury repair procedures have a high risk for postsurgical complication development. Future Directions: Negative pressure therapy modalities can offer clinicians additional adjunctive and cost-effective tools for the management of the trauma care patient, as these systems can be utilized during both the DCS and the postoperative injury management phases of trauma care.

A bimodal nomogram as an adjunct tool to reduce unnecessary breast biopsy following discordant ultrasonic and mammographic BI-RADS assessment.

To develop a bimodal nomogram to reduce unnecessary biopsies in breast lesions with discordant ultrasound (US) and mammography (MG) Breast Imaging Reporting and Data System (BI-RADS) assessments. This retrospective study enrolled 706 women following opportunistic screening or diagnosis with discordant US and MG BI-RADS assessments (where one assessed a lesion as BI-RADS 4 or 5, while the other assessed the same lesion as BI-RADS 0, 2, or 3) from two medical centres between June 2019 and June 2021. Univariable and multivariable logistic regression analyses were used to develop the nomogram. DeLong's and McNemar's tests were used to assess the model's performance. Age, MG features (margin, shape, and density in masses, suspicious calcifications, and architectural distortion), and US features (margin and shape in masses as well as calcifications) were independent risk factors for breast cancer. The nomogram obtained an area under the curve of 0.87 (95% confidence interval (CI), 0.83-0.91), 0.91 (95% CI, 0.87 - 0.96), and 0.92 (95% CI, 0.86-0.98) in the training, internal validation, and external testing samples, respectively, and demonstrated consistency in calibration curves. Coupling the nomogram with US reduced unnecessary biopsies from 74 to 44% and the missed malignancies rate from 13 to 2%. Similarly, coupling with MG reduced missed malignancies from 20 to 6%, and 63% of patients avoided unnecessary biopsies. Interobserver agreement between US and MG increased from - 0.708 (poor agreement) to 0.700 (substantial agreement) with the nomogram. When US and MG BI-RADS assessments are discordant, incorporating the nomogram may improve the diagnostic accuracy, avoid unnecessary breast biopsies, and minimise missed diagnoses. The nomogram developed in this study could be used as a computer program to assist radiologists with detecting breast cancer and ensuring more precise management and improved treatment decisions for breast lesions with discordant assessments in clinical practice. • Coupling the nomogram with US and mammography improves the detection of breast cancers without the risk of unnecessary biopsy or missed malignancies. • The nomogram increases mammography and US interobserver agreement and enhances the consistency of decision-making. • The nomogram has the potential to be a computer program to assist radiologists in identifying breast cancer and making optimal decisions.

Morphological diagnosis of hematologic malignancy using feature fusion-based deep convolutional neural network

Leukemia is a cancer of white blood cells characterized by immature lymphocytes. Due to blood cancer, many people die every year. Hence, the early detection of these blast cells is necessary for avoiding blood cancer. A novel deep convolutional neural network (CNN) 3SNet that has depth-wise convolution blocks to reduce the computation costs has been developed to aid the diagnosis of leukemia cells. The proposed method includes three inputs to the deep CNN model. These inputs are grayscale and their corresponding histogram of gradient (HOG) and local binary pattern (LBP) images. The HOG image finds the local shape, and the LBP image describes the leukaemia cell's texture pattern. The suggested model was trained and tested with images from the AML-Cytomorphology_LMU dataset. The mean average precision (MAP) for the cell with less than 100 images in the dataset was 84%, whereas for cells with more than 100 images in the dataset was 93.83%. In addition, the ROC curve area for these cells is more than 98%. This confirmed proposed model could be an adjunct tool to provide a second opinion to a doctor.

Predictors and Trends in First-Trimester Hemoglobin A1c Screening in New York City, 2009 to 2017.

Glycated hemoglobin is an adjunct tool in early pregnancy to assess glycemic control. We examined trends and maternal predictors for those who had A1c screening in early pregnancy using hospital discharge and vital registry data between 2009 and 2017 linked with the New York City A1C Registry (N = 798,312). First-trimester A1c screening increased from 2.3% in 2009 to 7.7% in 2017. The likelihood of screening became less targeted to high-risk patients over time, with a decrease in mean A1c values from 5.8% (95% confidence interval [CI]: 5.8, 5.9) to 5.3 (95% CI: 5.3, 5.4). The prevalence of gestational diabetes mellitus increased while testing became less discriminate for those with high-risk factors, including pregestational type 2 diabetes, chronic hypertension, obesity, age over 40 years, as well as Asian or Black non-Hispanic race/ethnicity. KEY POINTS: · First-trimester A1c screening increased from 2.3% in 2009 to 7.7% in 2017 in New York City.. · The likelihood of screening became less targeted to high-risk patients over time.. · The prevalence of gestational diabetes mellitus increased, while testing became less discriminate..

Exploring the Potential of ChatGPT-4 in Responding to Common Questions About Abdominoplasty: An AI-Based Case Study of a Plastic Surgery Consultation.

With the increasing integration of artificial intelligence (AI) in health care, AI chatbots like ChatGPT-4 are being used to deliver health information. This study aimed to assess the capability of ChatGPT-4 in answering common questions related to abdominoplasty, evaluating its potential as an adjunctive tool in patient education and preoperative consultation. A variety of common questions about abdominoplasty were submitted to ChatGPT-4. These questions were sourced from a question list provided by the American Society of Plastic Surgery to ensure their relevance and comprehensiveness. An experienced plastic surgeon meticulously evaluated the responses generated by ChatGPT-4 in terms of informational depth, response articulation, and competency to determine the proficiency of the AI in providing patient-centered information. The study showed that ChatGPT-4 can give clear answers, making it useful for answering common queries. However, it struggled with personalized advice and sometimes provided incorrect or outdated references. Overall, ChatGPT-4 can effectively share abdominoplasty information, which may help patients better understand the procedure. Despite these positive findings, the AI needs more refinement, especially in providing personalized and accurate information, to fully meet patient education needs in plastic surgery. Although ChatGPT-4 shows promise as a resource for patient education, continuous improvements and rigorous checks are essential for its beneficial integration into healthcare settings. The study emphasizes the need for further research, particularly focused on improving the personalization and accuracy of AI responses. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .