Kappa Values Research Articles

Validity of self-reported Helicobacter pylori eradication treatment from questionnaire and interview surveys of the JPHC-NEXT study: comparison with prescription history from insurance claims data.

We aimed to evaluate the validity of self-administered questionnaire surveys and face-to-face interview surveys for the detection of Helicobacter pylori eradication therapy. Participants were a cohort, aged 40-74 years, living in three different locations of Japan, who took part in the baseline survey (2011-2012) of the Japan Public Health Center-based Prospective Study for the Next Generation (JPHC-NEXT). Five years after the baseline survey, a questionnaire and interview survey were independently conducted to determine the history of Helicobacter pylori eradication treatment over the 5-year period. Prescription of Helicobacter pylori eradication medications in national insurance claims data from the baseline survey to the 5-year survey was used as a reference standard. In total, 15,760 questionnaire surveys and 8,006 interview surveys were included in the analysis. There were 3,471 respondents to the questionnaire and 2,398 respondents to the interview who reported having received Helicobacter pylori eradication treatment within the past five years. Comparison of the questionnaire survey to national insurance claims data showed a sensitivity of 95.1% (2213/2328), specificity of 90.6% (12174/13432), positive predictive value of 63.8% (2213/3471), negative predictive value of 99.1% (12174/12289), and Cohen's Kappa value of 0.71. Respective values of the interview survey were 94.4% (1694/1795), 88.7% (5507/6211), 70.6% (1694/2398), 98.2% (5507/5608), and 0.74. Both the questionnaire and the interview showed high sensitivity, high specificity, and good agreement with the insurance claim prescriptions data. Some participants may have received eradication treatment without going through the public insurance claim database, resulting in a low positive predictive value.

Diagnostic performance of 3.0 T unenhanced Dixon water-fat separation coronary MR angiography in patients with low-to-intermediate risk of coronary artery disease

PurposeTo evaluate the diagnostic performance of 3.0 T unenhanced compressed-sensing sensitivity encoding (CS-SENSE) Dixon water-fat separation coronary MR angiography (CMRA) in patients with low-to-intermediate risk of coronary artery disease (CAD) and its ability to grade the severity of CAD based on Coronary Artery Disease Reporting and Data System (CAD-RADS). MethodsA total of 55 patients who was clinically evaluated as low-to-intermediate risk of CAD were finally included to undergo both 3.0 T CS-SENSE water-fat separation CMRA and coronary computed tomography angiography (CCTA), and 11 of them also underwent X-ray coronary angiography (CAG). The severity of coronary artery disease was graded in patients who had completed both CCTA and CMRA examinations by the use of CAD-RADS reports for the patients with stable chest pain, and the diagnostic consistency between the two approaches was evaluated. Diagnostic performance of CMRA was assessed using the combination of CCTA and CAG as the reference standard for excluding or confirming CAD respectively. ResultsThe sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy of 3.0 T unenhanced water-fat separation coronary MRA were 90.0%, 95.0%, 81.8%, 97.4% and 94.0% for a patient-based analysis respectively. In comparison with CCTA, 3.0 T Dixon water-fat separation CMRA demonstrated excellent consistency in grading the severity of coronary heart disease according to CAD-RADS (0.77 for kappa value). ConclusionIn the group of low-to-intermediate probability for CAD, 3.0 T unenhanced CS-SENSE Dixon water-fat separation CMRA can present satisfactory diagnostic performance for the exclusion of CAD with high sensitivity and negative predictive value as well as the evaluation of grading the severity of coronary artery disease.

Mangrove mapping and monitoring using remote sensing techniques towards climate change resilience

Mangroves are amongst the richest ecosystems in the world providing valuable goods and services to millions of people while enhancing the resilience of coastal communities against climate change induced hazards, especially island nations. However, these mangroves are severely affected by many anthropogenic activities. Therefore, understanding the spatial variability of mangroves in island nations is highly essential in the events of ongoing climatic change. Thus, this study assessed the use of remote sensing techniques and GIS to map and monitor mangrove cover change at selected sites, namely Le Morne and Ferney, on the tropical island of Mauritius. Freely available 2013 SPOT-5 and 2023 Sentinel 2A images were retrieved and processed using ArcGIS Pro tools and SNAP; mangroves were mapped based on Google Earth Pro historical imagery and ground truthing at the respective sites. Following the application of selected vegetation indices, GLCM and PCA analysis, mosaicked images were classified using the Random Trees algorithm. Kappa values of all the classified images were in the 90 s; Le Morne showed a significant increase in mangrove cover over the decadal scale with main class change from mudflat to mangroves. This study demonstrates how geo-spatial tools are crucial for monitoring mangroves as they provide spatially explicit and time sensitive information. Decision makers, researchers, and relevant stakeholders can utilize this data to bolster tailored mitigation and adaptation strategies at specific sites, thereby enhancing resilience to climate change.

A rapid and sensitive fluorescent chromatography with cloud system for MPXV point-of-care diagnosis

Monkeypox (mpox) is spreading around the world, and its rapid diagnosis is of great significance. In the present study, a rapid and sensitive fluorescent chromatography assisted with cloud system was developed for point-of-care diagnosis of mpox. To screen high affinity antibodies, nanoparticle antigen AaLS-A29 was generated by conjugating A29 onto scaffold AaLS. Immunization with AaLS-A29 induced significantly higher antibody titers and monoclonal antibodies were generated with the immunized mice. A pair of monoclonal antibodies, MXV 14 and MXV 15, were selected for fluorescence chromatography development. The Time-Resolved Fluorescence Immunoassay (TRFIA) was used to develop the chromatography assay. After optimization of the label and concentration of antibodies, a sensitive TRFIA assay with detection limit of 20 pg/mL and good repeatability was developed. The detection of the surrogate Vaccinia virus (VACA) strain Tian Tan showed that the TRFIA assay was more sensitive than the SYBR green I based quantitative PCR. In real samples, the detection result of this assay were highly consistent with the judgement of Quantitative Real-Time PCR (Concordance Rate = 90.48%) as well as the clinical diagnosis (Kappa Value = 0.844, P < 0.001). By combining the portable detection and online cloud system, the detection results could be uploaded and shared, making this detection system an ideal system for point-of-care diagnosis of mpox both in field laboratory and outbreak investigation.

Application of optical coherence tomography angiography in the assessment of diabetic macular edema staging and laser photocoagulation efficacy

ObjectiveThis study aimed to analyze the effect of optical coherence tomography angiography (OCTA) on diabetic macular edema (DME) staging and assess the efficacy of laser photocoagulation. MethodsEighty-six patients (141 eyes) with suspected DME who visited our hospital from August 2019 to March 2022 were selected and underwent fundus angiography and OCTA. The two examination methods were compared in terms of their efficacy in macular edema staging. Subsequently, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of OCTA in diagnosing DME were assessed using fundus angiography as the gold standard. In patients with clinically significant macular edema (CSME) treated with laser photocoagulation, the central concave non-perfused zone (FAZ), vascular density (VD), central macular retinal thickness (CRT), whole retinal blood flow density (FD-300), superficial capillary plexus (SCP), and deep capillary plexus (DCP) were measured using the OCTA 3 mm × 3 mm mode before treatment, at 3 months after treatment, and at 6 months after treatment. SCP, deep capillary plexus (DCP), blood flow density (VD), best corrected visual acuity (BCVA), and central retinal thickness (CRT) were recorded before treatment, 3 months after treatment, and 6 months after treatment. The correlation between BCVA and pre-treatment OCTA parameters at 6 months after treatment was analyzed using Pearson's correlation. ResultsFundus angiography was performed in 86 patients (141 eyes) with suspected DME. Of the 141 eyes, 44 had no leakage, 52 had diffuse edema, 40 had focal macular edema, and 5 had eyes ischemia. A total of 97 eyes showed CSME on fundus angiography. Using fundus angiography as the gold standard, OCTA exhibited a sensitivity of 97.94 %, a specificity of 63.64 %, and an accuracy of 87.23 % in diagnosing CSME. The Kappa value between OCTA and fundus angiography was 0.674. The receiver operating characteristic curve revealed that the area under the curve (AUC) of OCTA in diagnosing CSME was 0.808 (95 % confidence interval: 0.717–0.899). The BCVA was higher, while the CRT was lower in CSME patients at 3 and 6 months after treatment (P<0.05). No significant difference was observed in the OCTA parameters in CSME patients at 3 months after treatment compared with that before treatment (P>0.05). Similarly, no significant difference was found in the FD300 of CSME patients at 6 months after treatment compared with that before treatment (P>0.05). However, the FAZ area, DCP-VD (overall, central concave, and paracentral concave), and SCP-VD (overall, central concave, and paracentral concave) were higher in CSME patients at 6 months after treatment compared with that before treatment (P<0.05). Pearson's correlation showed that BCVA was positively correlated with pre-treatment FAZ area, DCP-VD, and SCP-VD (r>0, P<0.05), and negatively associated with CRT (r<0, P<0.05). ConclusionOCTA exhibited high sensitivity and specificity in diagnosis and staging DME. It adeptly captures the microvascular and visual changes in the central macular recess before and after laser photocoagulation therapy, which can quantitatively guide the follow-up treatment of DME.

The value of multimodality MR in T staging evaluation after neoadjuvant therapy for rectal cancer.

Surgery is the preferred treatment for rectal cancer, but surgical treatment alone sometimes does not achieve satisfactory results. To explore the value of multimodal Magnetic Resonance (MR) images in evaluating T staging of rectal cancer after neoadjuvant therapy and to compare and analyze with pathological results. This study retrospectively analyzed 232 patients with stage T3, T4 rectal cancer between January 1, 2017 and October 31, 2022. MR examination was performed within 3 days before surgery. Different MR sequences were used for mrT staging of rectal cancer after neoadjuvant therapy and compared with pathological pT staging. The accuracy of different MR sequences in evaluating T staging of rectal cancer was calculated, and the consistency between the two was analyzed by kappa test. The sensitivity, specificity, negative predictive value and positive predictive value of different MR sequences in evaluating rectal cancer invading mesorectal fascia after neoadjuvant therapy were calculated. A total of 232 patients with rectal cancer were included in the study. The accuracy of high-resolution T2 WI in evaluating T staging of rectal cancer after neoadjuvant therapy was 49.57%, and the Kappa value was 0.261. The accuracy of high-resolution T2WI combined with diffusion weighted imaging (DWI) in evaluating T staging of rectal cancer after neoadjuvant therapy was 61.64%, and the Kappa value was 0.411. The accuracy of high-resolution combined with DCE-MR images in evaluating T staging of rectal cancer after neoadjuvant therapy was 80.60%, and the Kappa value was 0.706. The sensitivity and specificity of high-resolution t2-weighted imaging (HR-T2WI) combined with dynamic contrast-enhancement magnetic resonance (DCE-MR) in evaluating the invasion of mesorectal fascia were 83.46% and 95.33%, respectively. Compared with HR-T2WI combined with DWI images for mrT staging of rectal cancer after neoadjuvant chemoradiotherapy (N-CRT), HR-T2WI combined with DCE-M has the highest accuracy in evaluating mrT staging of rectal cancer after neoadjuvant therapy (80.60%), and has a high consistency with pathological pT staging. It is the best sequence for T staging of rectal cancer after neoadjuvant therapy. At the same time, the sequence has high sensitivity and specificity in evaluating mesorectal fascia invasion, which can provide accurate perioperative information for the formulation of surgical plan.

Direct lingam and visibility graphs for analyzing brain connectivity in BCI.

The brain-computer interface (BCI) is a direct pathway of communication between the electrical activity of the brain and an external device. The present paper was aimed to investigate directed connectivity between different areas of the brain during motor imagery (MI)-based BCI. For this purpose, two methods were implemented including, Limited Penetrable Horizontal Visibility Graph (LPHVG) and Direct Lingam. The visibility graph (VG) is a robust algorithm for analyzing complex systems such as the brain. Direct Lingam uses a non-Gaussian model to extract causal links which is appropriate for analyzing large-scale connectivity. First, LPHVG map MI-EEG (electroencephalogram) signals into networks. After extracting the topological features of the networks, a support vector machine classifier was applied to categorize multi-classes MI. The network of all classes was found to be different from one another, and the kappa value of classification was 0.68. The degree sequence of LPHVG was calculated for each channel in order to obtain the direction of brain information flow. Transfer entropy (TE) is used to compute the relations of the channel degree sequence. Therefore, the directed graph between channels was formed. This method is called LPHVG_TE directed graph. The Bayesian network, also known as the Direct LiNGAM model, was implemented for the second method. Finally, images of the LPHVG and Direct Lingam were classified by convolutional neural network (CNN). In this study, Data sets 2a of BCI competition IV was used. The outcomes reveal that the brain network developed by LPHVG (92.7%) might be more effective to distinguish 4 classes of MI than the Direct Lingam (90.6%) and it was shown that graph theory has the potential to get better efficiency of BCI.

Interobserver Reliability of Cervical Vertebral Maturation Staging

Background: the accurate identification of a patient's maturation stage, particularly during the pubertal growth spurt, takes on heightened importance, particularly when addressing cases of dentoskeletal problem Active growth determination is crucial for orthodontic treatment. Objectives: The objective of this study was to determine interobserver reliability of Cervical Vertebrae Maturation (CVM) stagingMaterial and Methods: This cross-sectional correlational study, conducted in the Department of Orthodontics on 70 subjects. Ten orthodontic clinicians, including 4 orthodontists and 6 orthodontic residents, with no prior experience in CVM staging, underwent comprehensive training. The study involved assessing 70 lateral cephalograms with clear visibility of cervical vertebrae, evaluated by two independent examiners using the Baccetti et al. staging system. The collected data were analyzed using R programming, including descriptive statistics for age, Spearman's correlation, and Cohen's Kappa for assessing interobserver agreement, with stratification by gender. Results: The mean age of participants was 12.60±3.93 years, with 38 females (54.29%) and 32 males (45.71%). The study revealed varying levels of agreement between two observers for different CVM stages, ranging from perfect agreement in CS1 to strong agreement in CS3. CS4, CS5, and CS6 also showed varying agreement rates. Additionally, there was robust and statistically significant agreement (p&lt;0.001) between observers in cervical maturation staging, supported by a high Spearman's rho value (r=0.927). Furthermore, substantial agreement was observed with a Kappa value of 0.748, indicating a strong consensus in cervical staging assessments.Conclusion: Cervical vertebral maturation staging is reliable in term of inter-observers agreement. Keywords: Cervical vertebral maturation, Interobserver agreement, Growth assessment

A comparative spatial analysis of flood susceptibility mapping using boosting machine learning algorithms in Rathnapura, Sri Lanka

AbstractIdentifying flood‐prone areas is essential for preventing floods, reducing risks, and making informed decisions. A spatial database with 595 flood inventory and 13 flood predictors were used to implement five boosting algorithms: gradient boosting machine (GBM), extreme gradient boosting, categorical boosting, logit boost, and light gradient boosting machine (LGBM) to map flood susceptibility in Rathnapura while evaluating trained model's generalizing ability and assessing the feature importance in flood susceptibility mapping (FSM). The model performance was evaluated using the F1‐score, kappa index, and area under curve (AUC) method. The findings revealed that all the models were effective in identifying the overall flood susceptibility trends while LightGBM model had superior results (F1‐score = 0.907, Kappa value = 0.813 and AUC = 0.970), securing the top scores across all performance metrics compared to the other models (for testing dataset). Based on kappa evaluation, most of the models had finer performance (AUC min = 0.737) while LightGBM had moderate performance for predictions beyond the training region. According to the results, regions with lower altitudes and topographic roughness values, moderate rainfall, and proximity to rivers are more susceptible to flooding. This framework can be adapted for rapid FSM in data‐deficient regions.

Assessment of the Health Status of Old Trees of Platycladus orientalis L. Using UAV Multispectral Imagery

Assessing the health status of old trees is crucial for the effective protection and health management of old trees. In this study, we utilized an unmanned aerial vehicle (UAV) equipped with multispectral cameras to capture images for the rapid assessment of the health status of old trees. All trees were classified according to health status into three classes: healthy, declining, and severe declining trees, based on the above-ground parts of the trees. Two traditional machine learning algorithms, Support Vector Machines (SVM) and Random Forest (RF), were employed to assess their health status. Both algorithms incorporated selected variables, as well as additional variables (aspect and canopy area). The results indicated that the inclusion of these additional variables improved the overall accuracy of the models by 8.3% to 13.9%, with kappa values ranging from 0.166 and 0.233. Among the models tested, the A-RF model (RF with aspect and canopy area variables) demonstrated the highest overall accuracy (75%) and kappa (0.571), making it the optimal choice for assessing the health condition of old trees. Overall, this research presents a novel and cost-effective approach to assessing the health status of old trees.

A novel approach for intelligent diagnosis and grading of diabetic retinopathy

Diabetic retinopathy (DR) is a severe ocular complication of diabetes that can lead to vision damage and even blindness. Currently, traditional deep convolutional neural networks (CNNs) used for DR grading tasks face two primary challenges: (1) insensitivity to minority classes due to imbalanced data distribution, and (2) neglecting the relationship between the left and right eyes by utilizing the fundus image of only one eye for training without differentiating between them. To tackle these challenges, we proposed the DRGCNN (DR Grading CNN) model. To solve the problem caused by imbalanced data distribution, our model adopts a more balanced strategy by allocating an equal number of channels to feature maps representing various DR categories. Furthermore, we introduce a CAM-EfficientNetV2-M encoder dedicated to encoding input retinal fundus images for feature vector generation. The number of parameters of our encoder is 52.88 M, which is less than RegNet_y_16gf (80.57 M) and EfficientNetB7 (63.79 M), but the corresponding kappa value is higher. Additionally, in order to take advantage of the binocular relationship, we input fundus retinal images from both eyes of the patient into the network for features fusion during the training phase. We achieved a kappa value of 86.62% on the EyePACS dataset and 86.16% on the Messidor-2 dataset. Experimental results on these representative datasets for diabetic retinopathy (DR) demonstrate the exceptional performance of our DRGCNN model, establishing it as a highly competitive intelligent classification model in the field of DR. The code is available for use at https://github.com/Fat-Hai/DRGCNN.

Comparison of Land Cover Change Prediction Models: A Case Study in Kedungkandang District, Malang City

The infrastructure of Malang City is currently being directed towards the eastern and southeastern parts, Kedungkandang District. Infrastructure plays an important role in the aspect of land cover change, which raises the complexity of the emergence of urban forms and dynamics. This study compares three models, Artificial Neural Network (ANN), Logistic Regression (LR), and Multi-Criteria Evaluation (MCE), to predict changes in land cover in the Kedungkandang District using the Cellular Automata (CA) approach. The prediction results indicate that the ANN and MCE models have the highest overall Kappa values (prediction accuracy), while the ANN and LR models have the highest location-specific Kappa values. However, overall, the ANN model demonstrates the highest accuracy and performance among the other two models. This research makes a significant contribution to urban planning by highlighting the importance of using machine learning-based technology to predict land cover changes in Malang City, particularly in the Kedungkandang District. Stakeholders can leverage this technology to design more effective and sustainable infrastructure policies and implement preventive measures to mitigate the negative impacts of uncontrolled urban growth.

The role of early cerebral edema and hematoma assessment in aneurysmal subarachnoid hemorrhage (a-SAH) in predicting structural brain abnormalities in cognitive impairments-- case controlled study.

Early assessment and management of cerebral edema and hematoma following aneurysmal subarachnoid hemorrhage (a-SAH) can significantly impact clinical cognitive outcomes. However, current clinical practices lack predictive models to identify early structural brain abnormalities affecting cognition. To address this gap, we propose the development of a predictive model termed the a-SAH Early Brain Edema/Hematoma Compression Neural (Structural Brain) Networks Score System (SEBE-HCNNSS). In this study, 202 consecutive patients with spontaneous a-SAH underwent initial computed tomography (CT) or magnetic resonance imaging (MRI) scans within 24 hours of ictus with follow-up 2 months after discharge. Using logistic regression analysis (univariate and multivariate), we evaluated the association of clinically relevant factors and various traditional scale ratings with cognitive impairment (CI). Risk factors with the highest area under the curve (AUC) values were included in the multivariate analysis and least absolute shrinkage and selection operator (LASSO) analysis or Cox regression analysis. A total of 177 patients were enrolled in the study, and 43 patients were classified with a high SEBE-HCNNSS grade (3 to 5). After a mean follow-up of 2 months, 121 individuals (68.36%) with a-SAH and 3 control subjects developed incident CI. The CT inter-observer reliability of the SEBE-HCNNSS scale was high, with a Kappa value of 1. Furthermore, ROC analysis identified the SEBE-HCNNSS scale (OR 3.322, 95% CI 2.312-7.237, P=0.00025) as an independent predictor of edema, CI, and unfavorable prognosis. These results were also replicated in a validation cohort. Overall, the SEBE-HCNNSS scale represents a simple assessment tool with promising predictive value for CI and clinical outcomes post-a-SAH. Our findings indicate its practical utility as a prognostic instrument for risk evaluation after a-SAH, potentially facilitating early intervention and treatment.

ResNet101-SVM: Hybrid convolutional neural network for citrus fruits classification

Recognizing and classifying citrus fruits is a challenging yet crucial task for agriculture, food processing, and quality control. Classifying citrus fruits is challenging because of their wide variety, often with a similar flesh appearance, shape, and size. Therefore, efficient and effective approaches are required for accurate identification. Our study focused on efficiently identifying citrus fruit types by utilizing a hybrid ResNet101-SVM model. ResNet101-SVM is the combination of the feature extraction capabilities of the ResNet101 with the classification power of SVM. This hybrid approach leverages the strengths of both deep learning (feature extraction) and traditional machine learning (SVM classification) to improve the accuracy and robustness of citrus fruit classification. The model outperformed the standard ResNet101 model across various performance metrics, achieving impressive accuracy, sensitivity, specificity, precision, F1 Score, MCC, and Kappa values of 99.81%, 99.81%, 99.8%, 99.82%, 0.18%, 99.81%, 99.80%, and 98.77%, respectively. This study holds significant promise for various applications, particularly in the domains of food processing and quality control.

Synthesizing Contrast-Enhanced MR Images from Noncontrast MR Images Using Deep Learning.

Recent developments in deep learning methods offer a potential solution to the need for alternative imaging methods due to concerns about the toxicity of gadolinium-based contrast agents. The purpose of the study was to synthesize virtual gadolinium contrast-enhanced T1-weighted MR images from noncontrast multiparametric MR images in patients with primary brain tumors by using deep learning. We trained and validated a deep learning network by using MR images from 335 subjects in the Brain Tumor Segmentation Challenge 2019 training data set. A held out set of 125 subjects from the Brain Tumor Segmentation Challenge 2019 validation data set was used to test the generalization of the model. A residual inception DenseNet network, called T1c-ET, was developed and trained to simultaneously synthesize virtual contrast-enhanced T1-weighted (vT1c) images and segment the enhancing portions of the tumor. Three expert neuroradiologists independently scored the synthesized vT1c images by using a 3-point Likert scale, evaluating image quality and contrast enhancement against ground truth T1c images (1 = poor, 2 = good, 3 = excellent). The synthesized vT1c images achieved structural similarity index, peak signal-to-noise ratio, and normalized mean square error scores of 0.91, 64.35, and 0.03, respectively. There was moderate interobserver agreement between the 3 raters, regarding the algorithm's performance in predicting contrast enhancement, with a Fleiss kappa value of 0.61. Our model was able to accurately predict contrast enhancement in 88.8% of the cases (scores of 2 to 3 on the 3-point scale). We developed a novel deep learning architecture to synthesize virtual postcontrast enhancement by using only conventional noncontrast brain MR images. Our results demonstrate the potential of deep learning methods to reduce the need for gadolinium contrast in the evaluation of primary brain tumors.

Can researchers trust ICD-10 coding of medical comorbidities in orthopaedic trauma patients?

The 10th revision of the International Classification of Diseases (ICD-10) coding system may prove useful to orthopaedic trauma researchers to identify and document populations based on comorbidities. However, its use for research first necessitates determination of its reliability. The purpose of this study was to assess the reliability of electronic medical record (EMR) ICD-10 coding of nonorthopaedic diagnoses in orthopaedic trauma patients relative to the gold standard of prospective data collection. Nonexperimental cross-sectional study. Level 1 Trauma Center. Two hundred sixty-three orthopaedic trauma patients from 2 prior prospective studies from September 2018 to April 2022. Prospectively collected data were compared with EMR ICD-10 code abstraction for components of the Charlson Comorbidity Index (CCI), obesity, alcohol abuse, and tobacco use (retrospective data). Percent agreement and Cohen's kappa reliability. Percent agreement ranged from 86.7% to 96.9% for all CCI diagnoses and was as low as 72.6% for the diagnosis "overweight." Only 2 diagnoses, diabetes without end-organ damage (kappa = 0.794) and AIDS (kappa = 0.798) demonstrated Cohen's kappa values to indicate substantial agreement. EMR diagnostic coding for medical comorbidities in orthopaedic trauma patients demonstrated variable reliability. Researchers may be able to rely on EMR coding to identify patients with diabetes without complications or AIDS. Chart review may still be necessary to confirm diagnoses. Low prevalence of most comorbidities led to high percentage agreement with low reliability. Level 1 diagnostic.

Evaluation of CLINITEST® Rapid Covid-19 + Influenza antigen test in a cohort of symptomatic patients in an emergency department.

Rapid management of patients with respiratory tract infections in hospital emergency departments is one of the main objectives since the concurrent circulation of respiratory viruses following the SARS-CoV-2 pandemic. The use of new combined point-of-care antigen tests for detecting influenza A/B and SARS-CoV-2 represents an advantage in response time over the molecular tests. The objective was to evaluate the suitability of the CLINITEST® Rapid Covid-19 + Influenza Antigen test (Siemens Healthineers, Germany) (RCIA test) by measuring the sensitivity, specificity, Cohen's kappa, and cut-off values. Nasopharyngeal samples were collected from a randomised group of symptomatic patients of all ages at emergency department during January-February 2023. In parallel, these patients were screened for influenza A/B, and SARS-CoV-2 using RT-PCR. The Ct (cycle threshold) values were collected for positive [RT-PCR (+) /RCIA test (+)] and false negative [(RT-PCR (+) /RCIA test (-)] samples. A subanalysis was performed in the paediatric population (< 16 years-old). We included 545 patients (55.8% females) with a median age of 7 years-old (IQR: 1-66.5). The RCIA test showed a sensitivity of 59.7% [95%CI: 46.9-67.33] for influenza A, 65.6% [95%CI: 49.5-80.3] for influenza B, and 76.9% [95%CI: 45.8-84.8] for SARS-CoV-2. The specificity was between 90.7%-99.7% with a moderate/high level of agreement with RT-PCR (kappa score: 0.6-0.8) for the three respiratory viruses included in the RCIA test. The sensitivity of the RCIA test is insufficient for screening of patients, including patients with low Ct values (Ct > 20). Despite its good specificity and Cohen's kappa value, its use as a screening test is not comparable to RT-PCR systems in the ED environment with a high number of false negative results.

Effectiveness and diagnostic accuracy of teledermatology for the assessment of skin conditions.

Teledermatology provides a platform for swift specialist advice without the potential need for face-to-face review. Our objectives were to investigate the effectiveness, accuracy and diagnostic concordance of the platform with regard to the remote management of skin conditions. We undertook a single-centre, retrospective chart review over a 1-year period, comprising a total of 1703 teledermatology referrals. Two physicians independently assessed the diagnostic concordance between telederm diagnosis (TD), in-person diagnosis (ID) and histopathological diagnosis (HD). There were a total of 1703 TD referrals, of which 341 were rejected, leaving 1362 referrals for evaluation. Sixty-five per cent of these referrals were managed remotely and discharged with advice, although 4.6% of these were later re-referred for an in-person review. A total of 20% of referrals were rejected, of which the majority was due to a lack of appropriate imaging. The total concordance of TD compared to ID was 76.4%. When comparing the TD and ID/HD, we obtained a Kappa value of 0.636 indicating substantial agreement. In terms of accuracy, there were 49 biopsy-proven skin cancers picked up by the service in this cohort of data. Of these, 61.2% were given an accurate diagnosis on first impression via teledermatology, 14.3% were given a different diagnosis but correctly categorised as skin cancer and 24.5% could not be assessed; however, they were triaged and escalated based upon clinical suspicion. Our study demonstrates that teledermatology is an effective platform in terms of diagnosis and remote management, with adequate diagnostic accuracy and concordance to in-person diagnosis.

Consistency in human papillomavirus type detection between self-collected vaginal specimens and physician-sampled cervical specimens.

With the rising need for accessible cervical cancer screening, self-sampling methods offer a promising alternative to traditional physician-led sampling. This study aims to evaluate the efficacy of the HygeiaTouch Self Sampling Kit for Women in detecting human papillomavirus (HPV) types and predicting cervical lesions. We studied the concordance in identifying high-risk HPV (hrHPV) types between samples collected by physicians and those self-collected by women using a self-sampling kit for validation. Women aged 21-65, fitting into specific categories based on their cervical health history were eligible. Cohen's kappa coefficient to gauge concordance between the two specimen types and relative accuracy metrics in identifying cervical intraepithelial neoplasia (CIN) were also calculated, with physician-sampled specimens serving as a reference. A total of 1210 participants from three institutes were involved. The self-sampling kit closely matched the physician-led method in terms of collecting valid specimens (100% vs. 100%), identifying hrHPV types (kappa: 0.75, 95% confidence interval [95% CI]: 0.72-0.79; agreement: 87.7%, 95% CI: 85.8-89.6) and predicting CIN grade 2 or worse (CIN2+) (relative sensitivity: 0.949, relative accuracy: 0.959). Kappa values varied between 0.71 and 0.83 for different hrHPV types and combinations, with an overall value 0.75 (95% CI: 0.72-0.79) signifying robust compatibility between the two methods. Our study underscores the potential of the HygeiaTouch Self Sampling Kit as a reliable, efficient, and user-friendly alternative to traditional sampling methods. This suggests that self-sampling could be pivotal in expanding cervical cancer screening accessibility and enhancing detection rates.

Adapting the Donabedian model in undergraduate nursing education: a modified Delphi study

BackgroundDonabedian conceptual and multidimensional framework has been used in several studies in an educational context. In this study, we aimed to adapt the Donabedian three-component model (structure, process, and outcome) in undergraduate nursing education. This conceptual framework provides a comprehensive image of nursing education institutions and can help to evaluate institutions by measuring different aspects of their performance. A comprehensive understanding of the various elements of an educational institution helps to develop a complete, appropriate relevant set of performance indicators.MethodsThis was a modified Delphi study. It had three rounds. The expert panel consisted of nursing faculty members and nursing Ph.D. students. In the first round, a questionnaire was designed based on interviews, focus groups, and a literature review. Experts rated their agreement with each element on a 5-point Likert scale in rounds two and three. The consensus level was set as 75%. The stability between rounds was also determined by calculating kappa coefficients. One Sample T-Test was also calculated for new items in round three.ResultsAll 55 items of the questionnaire were confirmed in the second round based on the consensus percentage of 75. Five new items were added to the third round based on comments in round two. Eventually, all elements except one were confirmed according to the consensus level, kappa values, means, and One-Sample T-Test in round three. The structure's key elements include staff (academic and non-academic); equipment; guidelines; resources and facilities; and students’ demographics and characteristics. Process key elements include communication; education; evaluation; cooperation; and consultation. Outcome key elements include knowledge development; nursing image; alumni’s outcome; students’ outcome; related medical centers’ performance; accreditation and evaluation results; and satisfaction.ConclusionsDifferent elements of a nursing education institution at the bachelor's level were determined. The results of this study can help related bodies to develop and implement a comprehensive and systematic evaluation. These results can also be a basis for making this model useful in other nursing courses or education in other fields.