Severity Classification Research Articles

Improved diabetic retinopathy severity classification using squeeze-and-excitation and sparse light weight multi-level attention u-net with transfer learning from xception.

Diabetic Retinopathy (DR) is a significant cause of vision loss in diabetic patients, making early detection and accurate severity classification essential for effective management and prevention. This study aims to develop an enhanced DR severity classification approach using advanced model architectures and transfer learning to improve diagnostic accuracy and support better patient care. We propose a novel model, Xception Squeeze-and-Excitation Sparse Lightweight Multi-Level Attention U-Net (XceSE_SparseLwMLA-UNet), designed to classify DR severity using fundus images from the Messidor 1 and Messidor 2 datasets. The XceSE_SparseLwMLA-UNet integrates several advanced mechanisms: the Squeeze-and-Excitation (SE) mechanism for adaptive feature recalibration, the Sparse Lightweight Multi-Level Attention (SparseLwMLA) mechanism for effective contextual information integration, and transfer learning from the Xception architecture to enhance feature extraction capabilities. The SE mechanism refines channel-wise feature responses, while SparseLwMLA enhances the model's ability to identify complex DR patterns. Transfer learning utilizes pre-trained weights from Xception to improve generalization across DR severity levels. The proposed XceSE_SparseLwMLA-UNet model demonstrates superior performance in DR severity classification, achieving higher accuracy and improved multi-class F1 scores compared to existing models. The model's color-coded segmentation outputs offer interpretable visual representations, aiding medical professionals in assessing DR severity levels. The XceSE_SparseLwMLA-UNet model shows promise for advancing early DR diagnosis and management by enhancing classification accuracy and providing valuable visual insights. Its integration of advanced architectural features and transfer learning contributes to better patient care and improved visual health outcomes.

A hybrid algorithm based on machine learning (LightGBM-Optuna) for road accident severity classification (case study: United States from 2016 to 2020)

A hybrid algorithm based on machine learning (LightGBM-Optuna) for road accident severity classification (case study: United States from 2016 to 2020)

The Depression, Anxiety, and Stress of Student-Athletes from a Pre- to Post-COVID-19 World.

This study explored differences in student-athletes' symptoms of depression, anxiety, and stress pre- to post-COVID-19-pandemic. The WHO reported a 25% increase in depression and anxiety rates worldwide, with young people disproportionately affected. Student-athletes face many stressors related to their sporting and academic feats, but what is not known is how the COVID-19 pandemic affected their experiences of symptoms of mental illness. A multiple-cohort cross-sectional study design was employed, and data collected using physical and online surveys. Participants (cohort 1 M age = 20.18 years, SD = 1.52; cohort 2 M age = 19.75 years, SD = 1.45) were recruited from UK universities (N = 807; 427 pre-pandemic cohort, 380 post-pandemic cohort). Results revealed statistically significant differences in mean depression (F (1, 805) = 23.92, p < 0.001), anxiety (F (1, 806) = 20.15, p < 0.001), and stress symptoms (F (1, 805) = 5.24, p = 0.022) scores between cohorts. Scores for the post-pandemic cohort were significantly higher than pre-pandemic, suggesting a worsening of symptom severity. Distributions of student-athletes across categories of symptom severity also worsened for depressive and anxiety symptoms post-pandemic and were skewed towards more severe categories. Symptoms of depression, anxiety, and stress were a concern pre-pandemic. Rates are higher in the post-pandemic cohort, suggesting a worsening of symptoms. These data add to evidence on student-athletes' symptoms of mental illness by exploring a UK sample and comparing scores pre- and post-pandemic.

Analyzing the impacts of watershed management interventions and scenarios on soil erosion reduction using InVEST model in Yezat watershed, North West Ethiopia

ABSTRACT Land degradation is a serious problem for Ethiopia's productive capacity for land resources. To reverse these environmental dynamics, slow down soil degradation, and raise smallholder farmers’ agricultural yields, watershed management interventions have been implemented in Ethiopia since the 1980s. Thus, this study was aimed at assessing the effects of watershed management interventions on soil erosion reduction by using the InVEST model in the Yezat watershed, north-west Ethiopia. The modified ecosystem services valuation model (InVEST) was employed to evaluate soil loss reduction in response to watershed management interventions. Different watershed management intervention scenarios (baseline scenarios, afforestation scenarios, soil/stone bund scenarios, and integrated scenarios) were also applied to evaluate the effectiveness of watershed management interventions on soil loss reduction. The result of the study indicated that a high amount of mean annual soil loss (111 tons ha− 1 year−1) was observed in 2000 in the study area due to the expansion of cultivated land and built-up area at the expense of forest, shrubland, and grassland. However, the mean annual soil loss decreased from 111 tons per year in 2000 to 79 tons per year in 2021 in the study area due to different watershed management interventions. Additionally, at the baseline scenario, the estimated mean annual soil loss of the watershed was 111 tons ha-1 yr-1. Conversely, it was reduced to 49.3 ton ha-1 yr-1 in the reforestation scenario, 16.49 ton ha-1 yr-1 in the soil bund scenario, and 8.26 ton ha-1 yr-1 in the integrated scenario. The watershed covered by the very severe soil erosion severity class was reduced in all watershed management intervention scenarios. Among others, the highest soil erosion reduction was observed in the integrated (bio-physical) scenarios of watershed management interventions. Therefore, watershed management interventions and scenarios were the best mechanisms for soil loss reduction.

CORRELATION BETWEEN DEPRESSION LEVEL AND LIFE QUALITY OF HIV PATIENTS AT POLY VCT REGIONAL GENERAL HOSPITAL PROF.DR. SOEKANDAR, MOJOKERTO

Life quality of HIV (Human Immunodeficiency Virus) patients is important to note because this infectious disease is chronic and progressive so that it has a wide impact on all aspects of life both physical, psychological, social, and spiritual. Psychosocial problems especially depression is more severe faced by patients so that it can reduce the life quality. The purpose of this study is to explain the relationship between depression and life quality in HIV patients. The design of this study used correlation analytic with non-probability sampling of consecutive types, conducted on June 23 until June 30, 2020, with 52 respondents. The research instrument used the WHO Life quality-BREF questionnaire to evaluate life quality and Beck Depression Inventory II to evaluate depression levels. The results of the study were tested with Spearman Rho and showed the level of depression in the non-depression category of (17.3%) 9 respondents in the mild category (13.5%) 7 respondents in the moderate category (32.7%) 17 respondents and in the severe category (36.5 %) 19 respondents and good life quality (42.3%) 22 respondents and poor category (57.7%) 30 respondents. From the results of the study obtained p value = (0,000) &lt;α (0.05), which showed that there was correlation between depression level and life quality of HIV patients at Poly VCT Regional General Hospital Prof. Dr. Soekandar, Mojokerto. Depression contributed to a decrease in life quality of HIV patients, psychological burdens on sufferers such as sufferers felt pessimistic about the future, seeing themselves as worthless could have a dominant effect on decreasing life quality of HIV patients.

Validation of the Emergency Department Work Index in a Pediatric Freestanding Community Emergency Department.

The Emergency Department Work Index (EDWIN) is a validated overcrowding score shown to correlate well with staff assessment of adult emergency department (ED) overcrowding and the potential need for diversion. It derives from the number of staffed ED beds, attending physicians on duty, patients within each triage category, and admitted patients. To date, no study has validated EDWIN in a pediatric community ED setting. We aim to determine if EDWIN correlates with established overcrowding measures and provider perception of overcrowding within a freestanding, community-based pediatric ED. In this prospective observational study at a freestanding, community-based pediatric ED, EDWIN was calculated hourly over 8 weeks throughout the year. EDWIN was compared with other objective and previously established ED metrics of overcrowding, including rates of patients who left without being seen (LWBS), average time from arrival to ED room, average length of stay (LOS), ED occupancy rates, and number of patients in the waiting room. Furthermore, EDWIN was compared with provider perception of overcrowding by surveying providers 6 times a day during the study period using novel, real-time, longitudinal, electronic health record-based survey distribution methodology. Spearman correlation coefficients were calculated to characterize the associations between EDWIN vs provider perception and EDWIN vs ED metrics. ANOVA and Tukey HSD were used to compare means of ED metrics of overcrowding across EDWIN severity categories. Five hundred eleven provider perception survey responses were collected from July 2022 through January 2023. EDWIN directly correlated with all measures of overcrowding, including provider perception of crowdedness (rho = 0.67), LWBS rates (rho = 0.44), average time from arrival to ED room (rho = 0.74), average LOS (rho = 0.70), ED occupancy rates (rho = 0.68), and number of patients in the waiting room (rho = 0.65). All findings were statistically significant (P < 0.05). Our findings suggest that EDWIN is an accurate tool to measure overcrowding in a freestanding, community-based pediatric ED.

Spirometry findings of chronic lung disease in high-altitude residents of Ladakh (&gt;11000 feet above sea level)

Ladakh is a hilly Himalayan dry desert, situated at an altitude of &gt;11000 feet. Studies have demonstrated that the spirometric values of high-altitude residents are significantly higher than those of low-landers. This is a retrospective observational study that analyzes the spirometry pattern in chronic lung diseases among people from Ladakh. Enrolled subjects were clinic-radiologically diagnosed and had at least one spirometry report. The spirometric parameters were analyzed for normal and abnormal patterns of lung function. The abnormal patterns were further classified into types of ventilator defects and their severity. A total of 122 cases were included, with 67 (55%) men. The mean age was 52.2±15.4 years. The most common diseases were chronic obstructive pulmonary disease (COPD) in 51 cases (41%), and asthma in 41 (33%). The median predicted percentage of forced vital capacity (FVC) was 116% (63-179%) with &gt;100% in 105 (85%) patients. The median predicted percentage of the forced expiratory volume in the 1st second (FEV1) was 113% (99-175%) with &gt;100% in 90 (74%) patients. FVC was reduced in 9 (7%) cases, normal in 62 (51%), and more than normal in 49 (42%), with 11 (9%) cases having &gt;150% of the predicted percentage. FEV1 was reduced in 9 (8%) cases, normal in 67 (55%), and more than normal in 46 (37%) cases, with &gt;150% predicted seen in 10 (8%) cases. Similarly, overall, the predicted percentages of both FVC and FEV1 were &gt;100% in all obstructive airway diseases as well as in the separate COPD and asthma subgroups. FVC and FEV1 amongst chronic lung disease patients from Ladakh were more than normal in the majority. These higher values of spirometry led to incorrect disease severity classifications and disease patterns. We propose that studies should be done to devise local reference equations for spirometry for Himalayan high-altitude residents of India.

Evaluation of the applicability of weak shoulder and arm sparing signs in amyotrophic lateral sclerosis by multiple neurologists and neurology residents: A single-center study.

Amyotrophic lateral sclerosis (ALS) exhibits selective muscle weakness. The weak shoulder and arm sparing signs, assessed by a single experienced neurologist, have been reported to be superior to previous signs in sensitivity and specificity. However, it is unknown whether the same results are observed when assessed by multiple neurologists. Subjects were retrospectively identified from our department's inpatient database from 2014 to 2023. Medical Research Council (MRC) scores of the deltoid (Del), biceps brachii (BB), triceps brachii (TB), and first dorsal interosseous (FDI) muscles were evaluated. The weak shoulder sign was defined as positive when Del was weaker than BB and TB. The arm sparing sign was defined as positive when both Del and FDI were weaker than BB and TB. Sensitivity was analyzed in all ALS patients and in subgroups based on the region of symptom onset, presence or absence of upper motor neuron (UMN) signs, and the Japanese ALS Severity Classification. Seventy-one patients with ALS were identified. Eight neurologists and three neurology residents evaluated each patient's MRC scores. The weak shoulder and arm sparing signs were observed in 72% and 48% of patients, respectively, with no significant difference in sensitivity across patient subgroups. The weak shoulder and arm sparing signs showed high and moderate sensitivity, respectively, consistent with a previous report, even when evaluated by multiple examiners. This expands the clinical utility and increases the reliability of these signs, potentially contributing to accurate ALS diagnosis when combined with other clinical features and objective assessments.

Guidelines How to Integrate Surgery and Targeted Therapy with Biologics for the Treatment of Hidradenitis Suppurativa: Delphi Consensus Statements from an Italian Expert Panel.

Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease characterized by recurrent and painful nodules and abscesses in intertriginous skin areas, which can progress to sinus tract formation, tissue destruction, and scarring. HS is highly debilitating and severely impairs the psychological well-being and quality of life of patients. The therapeutic approach to HS is based on medical therapy and surgery. First-line medical therapy includes topical antibiotics, systemic antibiotics, and biologics. Main surgical procedures include deroofing, local excision, and wide local excision. Despite the availability of multiple therapeutic options, the rates of disease recurrence and progression continue to be high. In recent years, the possibility of combining biologic therapy and surgery has raised considerable interest. In a clinical trial, the perioperative use of adalimumab has been associated with greater response rates and improved inflammatory load and pain, with no increased risk of postoperative infectious complications. However, several practical aspects of combined biologic therapy and surgery are poorly defined. In June 2022, nine Italian HS experts convened to address issues related to the integration of biologic therapy and surgery in clinical practice. To this purpose, the experts identified 10 areas of interest based on published evidence and personal experience: (1) patient profiling (diagnostic criteria, disease severity classification, assessment of response to treatment, patient-reported outcomes, comorbidities); (2) tailoring surgery to HS characteristics; (3) wide local excision; (4) presurgery biologic treatment; (5) concomitant biologic and surgical treatments; (6) pre- and postsurgery management; (7) antibiotic systemic therapy; (8) biologic therapy after radical surgery; (9) management of adverse events to biologics; and (10) management of postoperative infectious complications. Consensus between experts was reached using the Estimate-Talk-Estimate method (Delphi Method). The statements were subsequently presented to a panel of 27 HS experts from across Italy, and their agreement was assessed using the UCLA Appropriateness Method. This article presents and discusses the consensus statements.

The Relationship between Tooth Brushing Techniques and Gingival Status in Patients Who Wear Fixed Orthodontics at a Private Dental Clinic in Makassar City

Background: Dental and oral hygiene is an important factor that must be maintained by orthodontic users. An effort to maintain healthy teeth and mouth is by brushing your teeth. Patients who use a deaf orthodontic device must be able to maintain their oral cavity to avoid the onset of caries and marginal gingivitis. Because there is a relationship between the brushing technique and gingival status in patients who wear fixed orthodontics. Research objectives: To find out the relationship between tooth brushing techniques and gingival status in patients who wear fixed orthodontics at a private dental clinic in Makassar City in 2024. Methods: The type of research used in this study is quantitative research using a survey approach. Survey research is a research method that takes samples from a population. The data and information collected by the researcher came from respondents. Results : The results of the study were obtained that there were several patients who had gingival status with different categories, including the mild category as many as 5 gingiva indexes, and the moderate category as many as 4 gingiva indexes, while the severe category had 4 gingiva indexes. It can be seen that orthodontic users are very susceptible to experiencing poor oral hygiene caused by respondents who do not maintain their oral hygiene. Conclusion : There is a relationship between the tooth brushing technique and gingival status in patients who wear fixed orthodontics at a private dental clinic in Makassar City in 2024.

Discriminative validity and interpretability of the mood and feelings questionnaire

BackgroundUsing the Mood and Feelings Questionnaire (MFQ) to differentiate between depression severity levels remains unexplored. We explored the discriminative validity of the MFQ to identify an optimal cut-off MFQ score to distinguish between subthreshold-to-mild and moderate-to-severe depression severity levels. MethodsAn observational cross-sectional design was used in a sample (N = 67) of help-seeking youth (ages 13 to 18, inclusive) experiencing depressive symptoms. The MFQ was administered verbatim to youth by a research analyst over the phone. Youth were then grouped into subthreshold-to-mild or moderate-to-severe depression severity categories based on scores received on the Kiddie Schedule for Affective Disorders and Schizophrenia-Depression Rating Scale. Receiver Operating Characteristic curve analyses were conducted, with area under the curve (AUC) and Youden Index (J) as primary indices. We hypothesized that the lower limit of the 95 % confidence interval for the area under the curve would be ≥0.70. ResultsThe primary analysis yielded an AUC of 0.85 (95 % CI: 0.763–0.947) and an optimal cut-off of ≥43 (J = 0.60, positive predictive value = 91.4 %, negative predictive value = 62.5 %, sensitivity = 72.7 %, specificity = 87.0 %). LimitationsOur study collected a small sample, and as such cannot identify how subgroup classification (e.g., based on race or gender) may moderate outcomes. Further, unknown measurement error of the predictor and reference variable measures can bias the estimates. ConclusionsOur preliminary findings highlight the potential for the MFQ to support clinical decision-making relevant to adolescents experiencing varying severities of depressive symptoms in secondary care settings.

Thoracic computed tomography (CT) image-based identification and severity classification of COVID-19 cases using vision transformer (ViT)

In this research, we developed a two-stage deep learning (DL) model using Vision Transformer (ViT) to detect COVID-19 and assess its severity from thoracic CT images. In the first stage, we utilized a pre-trained ViT model (ViT_B/32) and a custom CNN model to classify CT images as COVID-19 or non-COVID-19. The ViT model achieved superior performance with a fivefold cross-validated accuracy of 99.7%, compared to the custom CNN’s 98%. In the second stage, we employed a ViT-based U-Net model (Vision Transformer for Biomedical Image Segmentation, VITBIS) to segment lung and infection regions in COVID-19 positive CT images, determining the infection severity. This model uses transformers with attention mechanisms in both the encoder and decoder. The lung segmentation network achieved an Intersection Over Union (IOU) of 95.8% and a sensitivity of 99.67%, while the lesion segmentation network attained an IOU of 94% and a sensitivity of 98.3%.

Treatment-induced anogenital melanosis is a very frequent finding in patients with vulvar lichen sclerosus.

Pigmented lesions such as melanosis have rarely been reported in patients with vulvar lichen sclerosus (VLS) that is typically characterized by hypopigmented lesions. We aimed to analyze systematically anogenital melanosis in a large cohort of VLS patients. We analyzed the clinical data of 198 female patients with VLS. The anogenital lesions of all patients were professionally photographed in a standardized position and illumination. Severity classification of architectural findings followed an easy-to-use clinical score. A modified Melasma Area and Severity Index and an image analysis software were used to evaluate the area and intensity of pigmentation. According to the clinical score, 79 (198/39.9%) patients showed grade 1 disease, 78 (198/39.4%) grade 2, 37 (198/18.7%) grade 3, and 4 (198/2%) grade 4 disease. About 111 (56.1%) of the 198 patients had anogenital melanosis with a median modified Melasma Area and Severity Index of 3.6 (0.4-14). Univariate analysis revealed that anogenital melanosis was positively correlated with the use of topical estrogens (P = .0018) and negatively correlated with the use of pulsed high-dose corticosteroids plus low-dose methotrexate (PHDC-LDM, P = .021). On multivariable analysis, the use of topical hormone therapy turned out to be a strong independent predictor for the presence of anogenital melanosis (odds ratio: 4.57, 95% confidence interval: 1.66-12.57, P = .0033), whereas PHDC-LDM use was an independent predictor for the absence of anogenital melanosis (odds ratio: 0.35, 95% confidence interval: 0.15-0.84, P = .018). The study includes the retrospective monocentric design. Anogenital melanosis is a very frequent and so far, under-reported clinical finding in VLS patients. It is likely caused by the use of topical estrogens employed for VLS treatment. In contrast, patients with more severe disease and PHDC-LDM treatment appear to develop less likely anogenital melanosis.

Spectral sensor‐based device for real‐time detection and severity estimation of groundnut bud necrosis virus in tomato

AbstractA machine learning‐based approach was utilized to develop a device for groundnut bud necrosis virus (GBNV) disease severity detection and estimation in tomato plants (Solanum lycopersicum L.). The study involved inoculating tomato plants with GBNV, monitoring changes in morphological and spectral characteristics, evaluating machine learning algorithms (decision tree [DT] classifier) for analysis and classification of disease severity, and developing and validating a device for disease detection and severity estimation. Spectral data analysis revealed distinct patterns in reflectance, with notable peaks observed in the 680 and 760 nm bands, while reflectance remained low and constant beyond 900 nm. Machine learning techniques, specifically a DT model, were employed to classify disease severity based on spectral data with high accuracy (95.01% training accuracy and 93.65% testing accuracy). The model identified the near‐infrared band as highly correlated (correlation coefficient of 0.82) with disease severity. Furthermore, a compact handheld device integrating a spectral sensor, organic light‐emitting diode display, and Raspberry Pi 3B was developed for real‐time disease severity estimation. The device demonstrated robust performance, accurately predicting disease severity at different growth stages, even in the absence of visible symptoms. Additionally, disease severity percentages obtained via reverse transcription polymerase chain reaction were used to validate the accuracy of the device's estimations. Its responsive nature, with estimated response times ranging from milliseconds to seconds, facilitates timely interventions in agricultural settings. Overall, this interdisciplinary approach, combining spectral analysis, machine learning, and device development, presents a promising solution for efficient disease monitoring and management in agriculture, contributing to enhanced crop health and food security.

Monocentric Retrospective Analysis of Clinical Outcomes, Complications, and Adjacent Segment Disease in 507 Patients Undergoing Anterior Cervical Discectomy and Fusion for Degenerative Cervical Myelopathy

BackgroundDegenerative Cervical Myelopathy (DCM) is a leading cause of non-traumatic spinal cord injury. Surgery aims to arrest neurological decline and improve conditions, but controversies surround risks and benefits in elderly patients, outcomes in mild myelopathy, and the risk of adjacent segment disease (ASD). MethodsRetrospective data of patients who underwent ACDF for DCM in our hospital were collected. Patients were stratified by preoperative mJOA (mild, moderate, severe) and age (Under 70, Over 70). Clinical outcomes, complications, and ASD rate were analyzed. We evaluated the relationship between mJOA recovery rate and the risk of complications and various preoperative parameters. Results507 consecutive patients were included in the study, with a mean follow-up of 43.52 months (12-71). Improvement in all outcome variables was observed in mild, moderate and severe myelopathy categories, with elderly patients showing a lower improvement. Except for age, no other variable correlated with mJOA recovery rate. We observed 45 complications (11.1% of patients), with 14 in the U70 group and 31 in the O70 group (p-value<0.001). Age, Charlson Comorbidity index and ASA score were found to be predictors of complications. Fourteen patients (2.8% of total), mean age 54.2, developed radiological and clinical ASD. Most had cranial-level ASD with Pfirmann grade >= 2 before index surgery. ConclusionsMost myelopathic patients improve after ACDF. Elderly patients show a lower improvement and higher complication rates than younger counterparts. ASD rates are low, and younger patients with preexisting cranial level alterations are more susceptible.

Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images.

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature.

Assessment of the Quality of Cause-of-Death Data in Serbia for 2005-2019 Vital Statistics Performance Index Estimation.

We aimed to evaluate the quality of the cause of death (COD) concerning mortality patterns and completeness of death registration to identify areas for improvement in Serbia. COD data collected from the mortality register in Serbia from 2005 to 2019 (1540615 deaths) were analyzed with the software Analysis of National Causes of Death for Action. The Vital Statistics Performance Index for Quality (VSPI(Q)) is estimated for the overall COD data quality. The completeness of death certification was higher than 98%. Usable underlying COD was registered in 57%, 24.1% with an unusable and 18.6% with insufficiently specified COD. The VSPI(Q) was 67.2%, denoting medium quality. The typical error was using intermediate COD (24.7% of all deaths), while 13.2% and 8.5% of all garbage codes (GC) belonged to the Very High and High Severity classes. The leading underlying COD is unspecified cardiomyopathy. The analysis revealed that 39.1% of GC has been redistributed to non-communicable diseases, 2.5% to external causes and 1.1% to communicable diseases. In the 15 years' worth of data analyzed, the true underlying COD, in many cases, was ill-defined, indicating that COD data at the national level could be distorted. The additional and continuous professional education of medical students as well as physicians is needed. It should focus on the most common GC among the leading COD and acquiring skills in certifying external causes of death.

Stress, Anxiety and Depression in First-year Medical Undergraduates and Coping Strategies Employed during COVID-19-induced Online Learning: A Cross-sectional Study from North India

Abstract Background: The COVID-19 pandemic, due to its rising uncertainties, was expected to negatively impact the overall psychological functioning of students undergoing first year of medical professional education with changing teaching styles and environmental restraints. Objective: The objective was to assess the impact of this stressful period on the first-year Bachelor of Medicine and Bachelor of Surgery (MBBS) students at an Institute of National importance in North India, by measuring their various emotional states such as stress, anxiety, depression and coping strategies. Methods: This was a descriptive cross-sectional study undertaken in first-year MBBS students who were admitted in February 2021 due to delay in the admission process. Their physical classes were suspended due to the onset of second wave of COVID-19 and underwent a significant shift from conventional classes to a hybrid teaching module involving online classes, minimal face-to-face contact with teachers and almost no practical exposure which is in contrast to the conventional teaching practices. Data collection was done during and in the aftermath of second wave of COVID-19 using the Depression, Anxiety and Stress Scale (DASS-21) to assess emotional state and the Brief-Coping Orientation to Problems Experienced Inventory to measure the coping strategies employed. Results: One hundred and twenty-five students were approached and 80 consented to participate. The 80 students were analysed. Elevated depressive symptomatology scores were found in 46% of students, of which 14% had scores falling in severe category. More than 62% had elevated anxiety scores, of which 26% had severe range of scores, and 37% students had elevated stress scores, of which more than 10% had severe range of scores. Venting, humour and self-blame were found to be the predominant facets of coping styles employed and they had a significant statistical correlation with all three subscales of DASS-21. Conclusion: The study found a significant prevalence of depressive, anxiety and stress symptomatology. These findings emphasise the need for mental health interventions and support programmes for medical college MBBS students to help them cope with the psychological impact of academic and personal stressors.

Automated lost circulation severity classification and mitigation system using explainable Bayesian optimized ensemble learning algorithms

Lost circulation and mud losses cause 10 to 20% of the cost of drilling operations under extreme pressure and temperature conditions. Therefore, this research introduces an integrated system for an automated lost circulation severity classification and mitigation system (ALCSCMS). This proposed system allows decision makers to reliability predict lost circulation severity (LCS) based on a few drilling drivers before starting drilling operations. The proposed system developed and compared a total of 11 ensemble machine learning (EML) based on collection 65,377 observations, the data was pre-processed, cleaned, and normalized to be filtered using factor analysis. For each generated algorithm, the proposed system performed Bayesian optimization to acquire the best possible results. As a result, the optimized random forests (RF) model algorithm was the optimal model for classification at 100% classification accuracy based on testing data set. Mitigation optimization model based on genetic algorithm has been incorporated to convert high severe classes into acceptable classes of lost circulation. The system classifies the LCS into 5 classes where the classes from 2 to 4 are converted to be class 0 or 1 to minimize lost circulation severity by optimizing the input parameters. Therefore, the proposed model is reliable to predict and mitigate lost circulation during drilling operations. The main drivers that served as LCS inputs were explained using the SHapley Additive exPlanations (SHAP) approach.

Agreement of Different Drug-Drug Interaction Checkers for Proton Pump Inhibitors

ImportanceProton pump inhibitors (PPIs) are a widely prescribed class of drugs, potentially interacting with a large number of medicines, especially among older patients with multimorbidity and polypharmacy. Beyond summary of product characteristics (SPCs), interaction checkers (ICs) are routinely used tools to help clinicians in medication review interventions.ObjectiveTo assess the consistency of information on drugs potentially interacting with PPIs as reported in their SPCs and different ICs.Design, Setting, and ParticipantsThis cross-sectional study was conducted using data from SPCs for 5 PPIs (omeprazole, esomeprazole, lansoprazole, pantoprazole, and rabeprazole) and 5 ICs (ie, INTERCheck WEB, Micromedex, Lexicomp, Epocrates, and drugs.com). Information from the SPCs and the ICs were extracted between July 15 and 30, 2023.Main Outcomes and MeasuresThe main outcome was the level of agreement among SPCs and the 5 ICs in identifying drugs potentially interacting with PPIs and attributing drug-drug interaction (DDI) severity categories. The level of agreement was computed using Gwet AC1 statistic on the 5 ICs and by comparing 4-sets and 2-sets of ICs. As a sensitivity analysis, the level of agreement in listing PPI-related DDIs was evaluated using Cohen κ and Fleiss κ coefficients.ResultsConsidering SPCs and the 5 ICs, a total of 518 potentially interacting drugs with omeprazole were reported, 455 for esomeprazole, 433 for lansoprazole, 421 for pantoprazole, and 405 for rabeprazole. As compared with the ICs, the SPCs reported a much smaller number of drugs potentially interacting with PPIs, with proportions ranging from 2.7% (11 potentially interacting drugs) for rabeprazole to 7.6% (33 potentially interacting drugs) for lansoprazole of the total identified drugs at risk of interaction with a PPI. The overall level of agreement among the 5 ICs for identifying potential interactions was poor (from 0.23 [95% CI, 0.21-0.25] for omeprazole to 0.27 [95% CI, 0.24-0.29] for pantoprazole and 0.27 [95% CI, 0.25-0.29] for rabeprazole). Similarly, the level of agreement was low in 4-set and 2-set analyses as well as when restricting the analysis to the potential DDIs identified as severe (range, 0.30-0.32).Conclusions and RelevanceThis cross-sectional study found significant disagreement among different ICs and SPCs, highlighting the need to focus on standardizing DDI databases. Therefore, to ensure evaluation and prevention of clinically relevant DDIs, it is recommended to revise multiple ICs and consult with specialists, such as clinical pharmacologists, particularly for patients with complex medical conditions.