Diagnostic Categories Research Articles

Illness Anxiety Disorder: A Review of the Current Research and Future Directions.

We review recent evidence on Illness Anxiety Disorder (IAD), including risk factors and precipitants, diagnostic classification, clinical characteristics of the disorder, and assessment and treatment in both children and adults. IAD places a substantial burden on both individuals and society. Despite its impact, understanding of the disorder is lacking and debates remain about whether IAD should be classified as an anxiety disorder and whether it is distinct from Somatic Symptom Disorder. Cognitive behavioural therapy (CBT) is an effective treatment for IAD and there are multiple validated measures of health anxiety available. However, research on health anxiety in children and youth is limited. IAD is chronic, and debilitating, but when identified, it can be effectively treated with CBT. Research using DSM-5 IAD criteria is lacking, and more research is needed to better understand the disorder, particularly in children and youth.

Neuroprogression in bipolar disorder: why right is wrong.

The controversy on whether bipolar disorder is a neurodevelopmental versus a neuroprogressive illness is still around, despite some reductionistic claims that only one model is right. The current diagnostic classifications are not helpful to address this issue, and there is conflicting evidence in favor and against either model. In practice, though, understanding that many patients may show a progressive cognitive and functional decline which may be correlated with the number and severity of episodes may lead to better outcomes through early intervention strategies.

Assessment of inter- and intraobserver agreement for META score in distinguishing osteoporotic from multiple myeloma vertebral fractures.

To conduct an independent assessment of inter- and intraobserver agreement for the META score as a tool for differentiating osteoporotic vertebral fractures and multiple myeloma vertebral fractures. This is a retrospective observational study. The magnetic resonance imaging analysis was made by two independent spinal surgeons. We designated a Subjective assessment, in which the surgeon should establish a diagnostic classification for each vertebral fracture based on personal experience: secondary to osteoporosis, categorized as a benign vertebral fracture (BVF), or attributed to multiple myeloma, categorized a malign vertebral fracture (MVF). After a 90-day interval, both surgeons repeated the evaluations. For the next step, the observers should establish a diagnosis between BVF and MVF according to the META score system, and both observers repeated the evaluations after a 90-day interval. The intra and interobserver reliability of the Subjective evaluation was studied using the kappa (κ) test. Then, the META evaluations were paralleled using the intraclass correlation coefficient (ICC). A total of 220 patients who had the potential to participate in the study were initially enrolled, but after applying the exclusion criteria, 44 patients were included. Thirty-three patients had BVF, and 12 patients presented MVF. Interobserver agreement for both Subjective evaluations moments (initial and 90-days interval) found a slight agreement for both moments (0.35 and 0.40 respectively). Kappa test for both META evaluations moments (initial and 90-days interval) found a moderate interobserver agreement for both moments (0.54 and 0.48 respectively). It was observed that the ICC calculated for the Initial evaluation using META score was 0.680 and that in the 90-days interval was 0.726, indicating regular to good agreement. Kappa test for intraobserver agreements for the Subjective evaluation presented moderate agreement for both Surgeons. On the other side, Kappa test for intraobserver agreements for the META evaluation presented substantial agreement for both Surgeons. The Intraclass Correlation Coefficient of the META score found presented an almost perfect agreement for both Surgeons. Intra and interobserver agreement for both surgeons were unsatisfactory. The lack of consistent reproducibility by the same observer discourages and disfavors the routine use of the META score in clinical decision making, when potentially cases of multiple myeloma may be present.

Deep Learning Diagnostic Classification of Cervical Images to Augment Colposcopic Impression.

A deep learning classifier that improves the accuracy of colposcopic impression. Colposcopy images taken 56 seconds after acetic acid application were processed by a cervix detection algorithm to identify the cervical region. We optimized models based on the SegFormer architecture to classify each cervix as high-grade or negative/low-grade. The data were split into histologically stratified, random training, validation, and test subsets (80%-10%-10%). We replicated a 10-fold experiment to align with a prior study utilizing expert reviewer analysis of the same images. To evaluate the model's robustness across different cameras, we retrained it after dividing the dataset by camera type. Subsequently, we retrained the model on a new, histologically stratified random data split and integrated the results with patients' age and referral data to train a Gradient Boosted Tree model for final classification. Model accuracy was assessed by the receiver operating characteristic area under the curve (AUC), Youden's index (YI), sensitivity, and specificity compared to the histology. Out of 5,485 colposcopy images, 4,946 with histology and a visible cervix were used. The model's average performance in the 10-fold experiment was AUC = 0.75, YI = 0.37 (sensitivity = 63%, specificity = 74%), outperforming the experts' average YI of 0.16. Transferability across camera types was effective, with AUC = 0.70, YI = 0.33. Integrating image-based predictions with referral data improved outcomes to AUC = 0.81 and YI = 0.46. The use of model predictions alongside the original colposcopic impression boosted overall performance. Deep learning cervical image classification demonstrated robustness and outperformed experts. Further improved by including additional patient information, it shows potential for clinical utility complementing colposcopy.

Structured clinical interview for diagnosing obsessive-compulsive spectrum disorders.

BackgroundThere are several established structured diagnostic interviews that cover common mental disorders seen in general psychiatry clinics. The administration of more focused diagnostic interviews may be useful in specialty clinics, such as OCD clinics. A semi-structured clinician-administered interview for obsessive-compulsive spectrum disorders (SCID-OCSD) was developed and adapted for DSM-5/ICD-11 obsessive-compulsive and related disorders as well as other putative obsessive-compulsive spectrum conditions. ObjectiveTo introduce a semi-structured diagnostic interview for in-depth assessment of obsessive-compulsive spectrum disorders (OCSDs), and to report on its implementation in adults with primary OCD attending an OCD-specialized unit. MethodsPatients with primary OCD were interviewed using the SCID-OCSD. The SCID-OCSD assesses disorders drawn from several diagnostic categories that share some core features of obsessive-compulsive phenomenology and that are often comorbid in OCD (e.g., obsessive-compulsive related disorders, impulse-control disorders, and a spectrum of compulsive-impulsive conditions such as tics, eating disorders, non-suicidal self-injury, and behavioral addictions. Participants had to be at least moderately symptomatic on the Yale-Brown Obsessive-Compulsive Severity scale (YBOCS, i.e., a total score ≥ 14) to be included in the current study. ResultsOne hundred and one adult patients with current OCD (n = 101, 37 men and 64 women), took part in the study. Forty-two participants (n = 42) had OCD and one or more current or past comorbid OCSDs, with excoriation (skin-picking) disorder (n = 16) and body dysmorphic disorder (n = 14) being the most common. Nine (n = 9) participants reported a history of non-suicidal self-injury, and 6 participants reported a history of comorbid tics. ConclusionsIn OCD clinics, the SCID-OCSD may help diagnose the full range of putative OCSDs, and so facilitate treatment planning and research on these conditions.

Differential diagnosis of thyroid nodules using heterogeneity quantification software on ultrasound images: correlation with the Bethesda system and surgical pathology

Ultrasonography (US)-guided fine-needle aspiration cytology (FNAC) is the primary modality for evaluating thyroid nodules. However, in cases of atypia of undetermined significance (AUS) or follicular lesion of undetermined significance (FLUS), supplemental tests are necessary for a definitive diagnosis. Accordingly, we aimed to develop a non-invasive quantification software using the heterogeneity scores of thyroid nodules. This cross-sectional study retrospectively enrolled 188 patients who were categorized into four groups according to their diagnostic classification in the Bethesda system and surgical pathology [II-benign (B) (n = 24); III-B (n = 52); III-malignant (M) (n = 54); V/VI-M (n = 58)]. Heterogeneity scores were derived using an image pixel-based heterogeneity index, utilized as a coefficient of variation (CV) value, and analyzed across all US images. Differences in heterogeneity scores were compared using one-way analysis of variance with Tukey’s test. Diagnostic accuracy was determined by calculating the area under the receiver operating characteristic (AUROC) curve. The results of this study indicated significant differences in mean heterogeneity scores between benign and malignant thyroid nodules, except in the comparison between III-M and V/VI-M nodules. Among malignant nodules, the Bethesda classification was not observed to be associated with mean heterogeneity scores. Moreover, there was a positive correlation between heterogeneity scores and the combined diagnostic category, which was based on the Bethesda system and surgical cytology grades (R = 0.639, p < 0.001). AUROC for heterogeneity scores showed the highest diagnostic performance (0.818; cut-off: 30.22% CV value) for differentiating the benign group (normal/II-B/III-B) from the malignant group (III-M/V&VI-M), with a diagnostic accuracy of 72.5% (161/122). Quantitative heterogeneity measurement of US images is a valuable non-invasive diagnostic tool for predicting the likelihood of malignancy in thyroid nodules, including AUS or FLUS.

Irregular word reading as a marker of semantic decline in Alzheimer’s disease: implications for premorbid intellectual ability measurement

BackgroundIrregular word reading has been used to estimate premorbid intelligence in Alzheimer’s disease (AD) dementia. However, reading models highlight the core influence of semantic abilities on irregular word reading, which shows early decline in AD. The primary objective of this study is to ascertain whether irregular word reading serves as an indicator of cognitive and semantic decline in AD, potentially discouraging its use as a marker for premorbid intellectual abilities.MethodSix hundred eighty-one healthy controls (HC), 104 subjective cognitive decline, 290 early and 589 late mild cognitive impairment (EMCI, LMCI) and 348 AD participants from the Alzheimer’s Disease Neuroimaging Initiative were included. Irregular word reading was assessed with the American National Adult Reading Test (AmNART). Multiple linear regressions were conducted predicting AmNART score using diagnostic category, general cognitive impairment and semantic tests. A generalized logistic mixed-effects model predicted correct reading using extracted psycholinguistic characteristics of each AmNART words. Deformation-based morphometry was used to assess the relationship between AmNART scores and voxel-wise brain volumes, as well as with the volume of a region of interest placed in the left anterior temporal lobe (ATL), a region implicated in semantic memory.ResultsEMCI, LMCI and AD patients made significantly more errors in reading irregular words compared to HC, and AD patients made more errors than all other groups. Across the AD continuum, as well as within each diagnostic group, irregular word reading was significantly correlated to measures of general cognitive impairment / dementia severity. Neuropsychological tests of lexicosemantics were moderately correlated to irregular word reading whilst executive functioning and episodic memory were respectively weakly and not correlated. Age of acquisition, a primarily semantic variable, had a strong effect on irregular word reading accuracy whilst none of the phonological variables significantly contributed. Neuroimaging analyses pointed to bilateral hippocampal and left ATL volume loss as the main contributors to decreased irregular word reading performances.ConclusionsWhile the AmNART may be appropriate to measure premorbid intellectual abilities in cognitively unimpaired individuals, our results suggest that it captures current semantic decline in MCI and AD patients and may therefore underestimate premorbid intelligence. On the other hand, irregular word reading tests might be clinically useful to detect semantic impairments in individuals on the AD continuum.

Phage-Displayed SH2 Domain Libraries: From Ultrasensitive Tyrosine Phosphoproteome Probes to Translational Research.

Tyrosine phosphorylation is a critical regulator of cell signaling. A large fraction of the tyrosine phosphoproteome, however, remains uncharacterized, largely due to a lack of robust and scalable methods. The Src homology 2 (SH2) domain, a structurally conserved protein domain present in many intracellular signal-transducing proteins, naturally binds phosphorylated tyrosine (pTyr) residues, providing an ideal scaffold for the development of sensitive pTyr probes. Its modest affinity, however, has greatly limited its application. Phage display is an in vitro technique used for identifying ligands for proteins and other macromolecules. Using this technique, researchers have been able to engineer SH2 domains to increase their affinity and customize their specificity. Indeed, highly diverse phage display libraries have enabled the engineering of SH2 domains as affinity-purification (AP) tools for proteomic analysis as well as probes for aberrant tyrosine signaling detection and rewiring, and represent a promising class of novel diagnostics and therapeutics. This review describes the unique structure-function characteristics of SH2 domains, highlights the fundamental contribution of phage display in the development of technologies for the dissection of the tyrosine phosphoproteome, and highlights prospective uses of SH2 domains in basic and translational research.

Evaluating the risk of malignancies of the diagnostic categories proposed by the World Health Organization System for Reporting Lung Cytopathology: a two-year single institutional experience.

Introduction The WHO System of Reporting Lung Cytopathology proposed a 5-tiered system in 2023. We report the risk of malignancies (ROMs) of bronchial washing/lavage and percutaneous fine needle aspiration (FNA) specimens. We also evaluated the change of ROMs when image correlation is required. Methods Lung cytology cases in 2021 and 2022 with histologic follow-up were included. CT reports were reviewed to identify cases with a solid nodule/tumor but benign cytological findings. These were reassigned from the "Benign" to "Non-diagnostic" category, and the ROMs were re-estimated. Results A total of 1031 bronchial washing/lavage and 206 FNAs were identified. The ROMs of bronchial washing/lavage were "Non-diagnostic" 56.5% (13/23), "Benign" 41.9% (320/764), "Atypical" 71.7% (71/99), "Suspicious for malignancy" 94.7% (72/76), and "Malignant" 100% (70/70). The ROMs of FNAs were "Non-diagnostic" 66% (33/50), "Benign" 58.2% (39/67), "Atypical" 70% (28/40), "Suspicious for malignancy" 96.2% (25/26), and "Malignant" 100% (70/70). When image finding was considered, cases initially assigned as "Benign" were re-classified to "Non-diagnostic" with decreases in ROMs for the "Benign " category. Conclusions Malignancy risks associated with the WHO System of Reporting Lung Cytopathology diagnostic groups were reported. Image correlation for the "Benign" category led to a decrease in case number and ROM.

Effect of heatwaves on daily hospital admissions in Portugal, 2000–18: an observational study

Climate change has increased the frequency, intensity, and duration of heatwaves, posing a serious threat to public health. Although the link between high temperatures and premature mortality has been extensively studied, the comprehensive quantification of heatwave effects on morbidity remains underexplored. In this observational study, we assessed the relationship between heatwaves and daily hospital admissions at a county level in Portugal. We considered all major diagnostic categories and age groups (<18 years, 18-64 years, and ≥65 years), over a 19-year period from 2000 to 2018, during the extended summer season, defined as May 1, to Sept 30. We did a comprehensive geospatial analysis, integrating over 12 million hospital admission records with heatwave events indexed by the Excess Heat Factor (EHF), covering all 278 mainland counties. We obtained data from the Hospital Morbidity Database and E-OBS daily gridded meteorological data for Europe from 1950 to present derived from in-situ observations. To estimate the effect of heatwaves on hospital admissions, we applied negative binomial regression models at both national and county levels. We found a statistically significant overall increase in daily hospital admissions during heatwave days (incidence rate ratio 1·189 [95% CI 1·179-1·198]; p<0·0001). All age groups were affected, with children younger than 18 years being the most affected (21·7% [20·6-22·7] increase in admissions; p<0·0001), followed by the working-age (19·7% [18·7-20·7]; p<0·0001) and elderly individuals (17·2% [16·2-18·2]; p<0·0001). All 25 major disease diagnostic categories showed significant increases in hospital admissions, particularly burns (34·3% [28·7-40·1]; p<0·0001), multiple significant trauma (26·8% [22·2-31·6]; p<0·0001), and infectious and parasitic diseases (25·4% [23·5-27·3]; p<0·0001). We also found notable increases in endocrine, nutritional, and metabolic diseases (25·1% [23·4-26·8]; p<0·0001), mental diseases and disorders (23·0% [21·1-24·8]; p<0·0001), respiratory diseases (22·4% [21·2-23·6]; p<0·0001), and circulatory system disorders (15·8% [14·7-16·9]; p<0·0001). Our results provide statistically significant evidence of the association between heatwaves and increased hospitalisations across all age groups and for all major causes of disease. To our knowledge, this is the first study to estimate the full extent of heatwaves' impact on hospitalisations using the EHF index over a 19-year period, encompassing an entire country, and spanning 25 disease categories during multiple heatwave events. Our data offer crucial information to guide policy makers in effectively and efficiently allocating resources to address the profound health-care consequences resulting from climate change. None.

Application of 2022 diagnostic criteria and risk classification for acute myeloid leukemia: A study utilizing open-source data

Application of 2022 diagnostic criteria and risk classification for acute myeloid leukemia: A study utilizing open-source data

Automatic classification of spinal osteosarcoma and giant cell tumor of bone using optimized DenseNet

ObjectiveThis study aims to explore an optimized deep-learning model for automatically classifying spinal osteosarcoma and giant cell tumors. In particular, it aims to provide a reliable method for distinguishing between these challenging diagnoses in medical imaging. MethodsThis research employs an optimized DenseNet model with a self-attention mechanism to enhance feature extraction capabilities and reduce misclassification in differentiating spinal osteosarcoma and giant cell tumors. The model utilizes multi-scale feature map extraction for improved classification accuracy. The paper delves into the practical use of Gradient-weighted Class Activation Mapping (Grad-CAM) for enhancing medical image classification, specifically focusing on its application in diagnosing spinal osteosarcoma and giant cell tumors. The results demonstrate that the implementation of Grad-CAM visualization techniques has improved the performance of the deep learning model, resulting in an overall accuracy of 85.61%. Visualizations of images for these medical conditions using Grad-CAM, with corresponding class activation maps that indicate the tumor regions where the model focuses during predictions. ResultsThe model achieves an overall accuracy of 80% or higher, with sensitivity exceeding 80% and specificity surpassing 80%. The average area under the curve AUC for spinal osteosarcoma and giant cell tumors is 0.814 and 0.882, respectively. The model significantly supports orthopedics physicians in developing treatment and care plans. ConclusionThe DenseNet-based automatic classification model accurately distinguishes spinal osteosarcoma from giant cell tumors. This study contributes to medical image analysis, providing a valuable tool for clinicians in accurate diagnostic classification. Future efforts will focus on expanding the dataset and refining the algorithm to enhance the model's applicability in diverse clinical settings.

EXERCISE PHYSIOLOGY SERVICES FOR INPATIENTS IN AN ACUTE TRANSDIAGNOSTIC MENTAL HEALTH SERVICE

BACKGROUND &amp; AIMS Exercise interventions are increasingly utilised in mental health services to mitigate the poor physical health and elevated health risks seen in mental illness. To-date much of the research has focused on specific diagnostic classifications, rather than the commonly occurring transdiagnostic presentation. We sought to determine the feasibility of exercise physiology services for inpatients in an acute, transdiagnostic mental health service and whether general affect changed following exercise sessions. METHODS Data was collected from service records over a period spanning January 2020 – August 2022, and patient demographics and service information were explored. Additionally, a subset of inpatients (n= 108), reported perceptions of exercise and associations between variables were assessed between- and within-person. RESULTS During the 32-month audit 371 inpatients engaged, mean age 36 years (SD=12.33, range = 18-69) and mean length of stay was 36 days (SD=45, range = 3-445). The most frequent principal diagnosis was Schizophrenia or other psychotic disorder (38.4%), with 72% of inpatients had multiple psychiatric diagnoses. Physical health comorbidity was common, with 56% of patients having physical health diagnoses, predominantly cardiometabolic diseases (n= 94). There were 1386 exercise service engagements, with patient engagement ranging from 1-36 exercise session during their stay, of which 98.7% were in the hospital gym. There was a significant positive change in affect, with exertion was not related to changes in affect, however those who exercised for greater than 30min saw larger positive changes in affect. CONCLUSION Embedding Clinical Exercise Physiology services within mental healthcare settings can provide tailored exercise interventions for people with transdiagnostic presentations to address both physical and mental health outcomes. Participation in—and importantly, enjoyment of—exercise was linked to changes in general affect and may present a novel and important component of acute mood support for those in inpatient services.

The Use of Generative Adversarial Network and Graph Convolution Network for Neuroimaging-Based Diagnostic Classification

Functional connectivity (FC) obtained from resting-state functional magnetic resonance imaging has been integrated with machine learning algorithms to deliver consistent and reliable brain disease classification outcomes. However, in classical learning procedures, custom-built specialized feature selection techniques are typically used to filter out uninformative features from FC patterns to generalize efficiently on the datasets. The ability of convolutional neural networks (CNN) and other deep learning models to extract informative features from data with grid structure (such as images) has led to the surge in popularity of these techniques. However, the designs of many existing CNN models still fail to exploit the relationships between entities of graph-structure data (such as networks). Therefore, graph convolution network (GCN) has been suggested as a means for uncovering the intricate structure of brain network data, which has the potential to substantially improve classification accuracy. Furthermore, overfitting in classifiers can be largely attributed to the limited number of available training samples. Recently, the generative adversarial network (GAN) has been widely used in the medical field for its generative aspect that can generate synthesis images to cope with the problems of data scarcity and patient privacy. In our previous work, GCN and GAN have been designed to investigate FC patterns to perform diagnosis tasks, and their effectiveness has been tested on the ABIDE-I dataset. In this paper, the models will be further applied to FC data derived from more public datasets (ADHD, ABIDE-II, and ADNI) and our in-house dataset (PTSD) to justify their generalization on all types of data. The results of a number of experiments show the powerful characteristic of GAN to mimic FC data to achieve high performance in disease prediction. When employing GAN for data augmentation, the diagnostic accuracy across ADHD-200, ABIDE-II, and ADNI datasets surpasses that of other machine learning models, including results achieved with BrainNetCNN. Specifically, in ADHD, the accuracy increased from 67.74% to 73.96% with GAN, in ABIDE-II from 70.36% to 77.40%, and in ADNI, reaching 52.84% and 88.56% for multiclass and binary classification, respectively. GCN also obtains decent results, with the best accuracy in ADHD datasets at 71.38% for multinomial and 75% for binary classification, respectively, and the second-best accuracy in the ABIDE-II dataset (72.28% and 75.16%, respectively). Both GAN and GCN achieved the highest accuracy for the PTSD dataset, reaching 97.76%. However, there are still some limitations that can be improved. Both methods have many opportunities for the prediction and diagnosis of diseases.

Multi-Ethnic Norms for Volumes of Subcortical and Lobar Brain Structures Measured by Neuro I: Ethnicity May Improve the Diagnosis of Alzheimer's Disease1.

We previously demonstrated the validity of a regression model that included ethnicity as a novel predictor for predicting normative brain volumes in old age. The model was optimized using brain volumes measured with a standard tool FreeSurfer. Here we further verified the prediction model using newly estimated brain volumes from Neuro I, a quantitative brain analysis system developed for Korean populations. Lobar and subcortical volumes were estimated from MRI images of 1,629 normal Korean and 786 Caucasian subjects (age range 59-89) and were predicted in linear regression from ethnicity, age, sex, intracranial volume, magnetic field strength, and scanner manufacturers. In the regression model predicting the new volumes, ethnicity was again a substantial predictor in most regions. Additionally, the model-based z-scores of regions were calculated for 428 AD patients and the matched controls, and then employed for diagnostic classification. When the AD classifier adopted the z-scores adjusted for ethnicity, the diagnostic accuracy has noticeably improved (AUC = 0.85, ΔAUC = + 0.04, D = 4.10, p < 0.001). Our results suggest that the prediction model remains robust across different measurement tool, and ethnicity significantly contributes to the establishment of norms for brain volumes and the development of a diagnostic system for neurodegenerative diseases.

Development and verification of machine learning model based on anogenital distance, penoscrotal distance, and 2D:4D finger ratio before puberty to predict hypospadias classification.

To describe the anatomical abnormalities of hypospadias before puberty using current commonly used anthropometric index data and predict postoperative diagnostic classification. Children with hypospadias before puberty who were initially treated at Sichuan Provincial People's Hospital from April 2021 to September 2022 were selected. We recorded their preoperative penoscrotal distance, anogenital distance, 2D:4D finger ratio, and postoperative hypospadias classification. The receiver operating character curve was used for univariate analysis of the diagnostic predictive value of each index for hypospadias classification in the training set. Binary logistic regression, random forest, and support vector machine models were constructed. In addition, we also prospectively collected data from October 2022 to September 2023 as a test set to verify the constructed machine learning models. This study included 389 cases, with 50 distal, 167 midshaft, and 172 proximal cases. In the validation set, the sensitivity of the binary LR, RF, and SVM was 17%, 17% and 0% for identifying the distal type, 61%, 55% and 64% for identifying the midshaft type, and 56%, 60% and 48% for identifying the proximal type, respectively. The sensitivity of the three-classification RF and SVM models was 17% and 17% for distal type, 64% and 73% for midshaft type, 60% and 60% for proximal type, respectively. In the Testing set, the sensitivity of the binary LR, RF and SVM was 6%, 0% and 0% for identifying the distal type, 64%, 55% and 66% for identifying the midshaft type, and 48%, 62% and 39% for identifying the proximal type, respectively. The sensitivity of the three-classification RF and SVM models was 12% and 0% for distal type, 57% and 77% for midshaft type, and 65% and 53% for proximal type, respectively. Compared with binary classification models, the sensitivity of the three-classification models for distal type was not improved. Anogenital distance and penoscrotal distance have a favorable predictive value for midshaft and proximal hypospadias, among which AGD2, with higher test efficiency and stability, is recommended as the preferred anogenital distance indicator. The 2D:4D finger ratio (RadioL, RadioR) has little predictive value for hypospadias classification.

The relationship between acrochordons, obesity, and metabolic syndrome in the pediatric population: A retrospective cohort study.

Acrochordons or skin tags are common benign skin growths. Several studies explored the relationship between obesity and metabolic syndrome in adults but remains unexplored in children. This was a single-center retrospective cohort study of outpatient dermatology patients between 1 January 2000 to 1 January 2021. Children under 18 years old diagnosed with acrochordons using diagnostic codes International Classification of Diseases, 10th Revision (ICD-10) L91.8 and 9th Revision (ICD-9) 701.8 were included. We collected patient demographics, past medical history, laboratory values, vital signs, and physical exam. Body mass index (BMI) was calculated and stratified into categories based on the Center for Disease Control's BMI-for-Age Growth Charts. Metabolic syndrome was diagnosed when three of the five criteria were met. Data were propensity-matched and compared with NHANES (National Health and Nutrition Examination Survey), which offered a generalizable sample to the US population. Fifty-five patients under 18 years old with a diagnosis of acrochordons were mostly Caucasian (76%) and female (64%). The mean BMI was 27.3, with 49.5% categorized as obese and 20% as overweight. The mean age of diagnosis was 10.1 years. Acrochordon predominantly appeared in the axilla. In our cohort, three patients (5.5%) met the criteria for metabolic syndrome. The prevalence of obesity (42% vs. 21%), type 2 diabetes mellitus (4.8% vs. 0.6%), hyperlipidemia (8.1% vs. 0%), and hypertension (1.6% vs. 0%) was greater in our cohort compared with NHANES. Like the adult population, acrochordons may serve as marker for metabolic disease in the pediatric population.

A Method of Empirical Q-Matrix Validation for Multidimensional Item Response Theory

ABSTRACT A Q-matrix is a binary matrix that defines the relationship between items and latent variables and is widely used in diagnostic classification models (DCMs), and can also be adopted in multidimensional item response theory (MIRT) models. The construction process of the Q-matrix is typically carried out by experts in the subject area of the items and statistical procedures can be used to verify its suitability. In DCMs, different approaches have been proposed for validating the Q-matrix through iterative algorithms. This article presents a method of empirical Q-matrix validation for MIRT models. A simulation study and an application to real data of morphology skills of elementary students are conducted to examine the viability of the method. Relevant issues regarding the implementation of the method and the results obtained are discussed.

Introducing Diagnostic Classification Modeling as an Unsupervised Method for Screening Probable Eating Disorders.

Screening for eating disorders (EDs) is an essential part of the prevention and intervention of EDs. Traditional screening methods mostly rely on predefined cutoff scores which have limitations of generalizability and may produce biased results when the cutoff scores are used in populations where the instruments or cutoff scores have not been validated. Compared to the traditional cutoff score approach, the diagnostic classification modeling (DCM) approach can provide psychometric and classification information simultaneously and has been used for diagnosing mental disorders. In the present study, we introduce DCM as an innovative and alternative approach to screening individuals at risk of EDs. To illustrate the practical utility of DCM, we provide two examples: one involving the application of DCM to examine probable ED status from the 12-item Short form of the Eating Disorder Examination-Questionnaire (EDE-QS) to screen probable thinness-oriented EDs and the Muscularity-Oriented Eating Test (MOET) to screen probable muscularity-oriented EDs.

Schneiderian First Rank Symptoms Significantly Predict a Dissociative Disorder Diagnosis in Psychiatric In-Patients

ABSTRACT Previous empirical studies on the relationship between psychotic symptoms and dissociative disorders focused on auditory hallucinations only or employed limited statistical analyses. We investigated whether the frequency of Schneiderian first rank symptoms (FRS) predicts the presence or absence of a dissociative disorder (DD). Psychiatric in-patients (n = 116) completed measures of dissociation, FRS and general psychological distress (GPD). DD diagnoses were confirmed by multidisciplinary teams or administering the Structured Clinical Interview for DSM-IV Dissociative Disorders-Revised (SCID-D-R). The FRS were recorded in the Multidimensional Inventory of Dissociation (MID) and a mean score obtained for 35 relevant items: Voices arguing, voices commenting, made feelings, made impulses, made actions, influences on body, thought withdrawal, and thought insertion. A global severity index (GSI) of GPD was obtained from the Symptom Checklist–90–Revised (SCL-90-R). Logistic regression models examined whether FRS predict diagnostic classification of patients under a DD (n = 16) or not (n = 100), controlling for GSI. The overall fit of the model was significant (p = .0002). DD was correctly classified using frequency of FRS, controlling for GSI. The latter was moderately associated with FRS (r = 0.56). FRS more than doubled the odds of a DD diagnosis (odds = 2.089; 95% CI = 1.409–3.098; correct classification rate 87.1%). The study provides convincing evidence that FRS are closely related to DDs. FRS should alert clinicians to consider DDs in differential diagnosis of psychiatric in-patients. Future research should analyze whether FRS also predict a diagnosis of schizophrenia or other psychiatric disorders.