Prediction of estimated risk for bipolar disorder using machine learning and structural MRI features.

BACKGROUND: Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features. METHODS: Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar). RESULTS: For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11-0.361) and a balanced accuracy of 63.1% (95% CI 55.9-70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI -0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6-67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance. CONCLUSIONS: Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.

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

PURPOSE OF REVIEW: 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. RECENT FINDINGS: 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.

Differentiating anorexia nervosa and atypical anorexia nervosa with absolute weight cut-offs results in a skewed distribution for premorbid weight among youth.

OBJECTIVE: Anorexia nervosa (AN) and atypical AN are conceptualized as distinct illnesses, despite similar characteristics and sequelae. Whereas DSM-5 differentiates youth with AN and atypical AN by the presence of clinical 'underweight' (i.e., 5th BMI percentile for age-and-sex (BMI%)), we hypothesized that using this weight cut-off to discern diagnoses creates a skewed distribution for premorbid weight. METHOD: Participants included hospitalized youth with AN (n = 165, 43.1%) and atypical AN (n = 218, 56.9%). Frequency analyses and chi-square tests assessed the distribution of premorbid BMI z-scores (BMIz) for diagnosis. Non-parametric Spearman correlations and Stepwise Linear regressions examined relationships between premorbid BMIz, admission BMIz, and weight loss in kg. RESULTS: Premorbid BMIz distributions differed significantly for diagnosis (p < .001), with an underrepresentation of 'overweight/obesity' (i.e., BMI% ≥ 85th) in AN. Despite commensurate weight loss in AN and atypical AN, patients with premorbid 'overweight/obesity' were 8.31 times more likely to have atypical AN than patients with premorbid BMI% < 85th. Premorbid BMIz explained 57% and 39% of the variance in admission BMIz and weight loss, respectively. DISCUSSION: Findings support a homogenous model of AN and atypical AN, with weight loss predicted by premorbid BMI in both illnesses. Accordingly, premorbid BMI and weight loss (versus presenting BMI) may better denote the presence of an AN-like phenotype across the weight spectrum. Findings also suggest that differentiating diagnoses with BMI% < 5th requires that youth with higher BMIs lose disproportionately more weight for an AN diagnosis. This is problematic given unique treatment barriers experienced in atypical AN. PUBLIC SIGNIFICANCE: Anorexia nervosa (AN) and atypical AN are considered distinct conditions in youth, with differential diagnosis hinging upon a presenting weight status of 'underweight' (i.e., BMI percentile for age-and-sex (BMI%) < 5th). In our study, youth with premorbid 'overweight/obesity' (BMI% ≥ 85th) disproportionately remained above this threshold, despite similar weight loss. Coupled with prior evidence for commensurate characteristics and sequelae in both diagnoses, we propose that DSM-5 differentiation of AN and atypical AN inadvertently reinforces weight stigma and may contribute to treatment disparities in atypical AN.

Premorbid body weight predicts weight loss in both anorexia nervosa and atypical anorexia nervosa: Further support for a single underlying disorder.

OBJECTIVE: For adolescents, DSM-5 differentiates anorexia nervosa (AN) and atypical AN with the 5th BMI-centile-for-age. We hypothesized that the diagnostic weight cut-off yields (i) lower weight loss in atypical AN and (ii) discrepant premorbid BMI distributions between the two disorders. Prior studies demonstrate that premorbid BMI predicts admission BMI and weight loss in patients with AN. We explore these relationships in atypical AN. METHOD: Based on admission BMI-centile < or ≥5th, participants included 411 female adolescent inpatients with AN and 49 with atypical AN from our registry study. Regression analysis and t-tests statistically addressed our hypotheses and exploratory correlation analyses compared interrelationships between weight loss, admission BMI, and premorbid BMI in both disorders. RESULTS: Weight loss in atypical AN was 5.6 kg lower than in AN upon adjustment for admission age, admission height, premorbid weight and duration of illness. Premorbid BMI-standard deviation scores differed by almost one between both disorders. Premorbid BMI and weight loss were strongly correlated in both AN and atypical AN. DISCUSSION: Whereas the weight cut-off induces discrepancies in premorbid weight and adjusted weight loss, AN and atypical AN overall share strong weight-specific interrelationships that merit etiological consideration. Epidemiological and genetic associations between AN and low body weight may reflect a skewed premorbid BMI distribution. In combination with prior findings for similar psychological and medical characteristics in AN and atypical AN, our findings support a homogenous illness conceptualization. We propose that diagnostic subcategorization based on premorbid BMI, rather than admission BMI, may improve clinical validity. PUBLIC SIGNIFICANCE: Because body weights of patients with AN must drop below the 5th BMI-centile per DSM-5, they will inherently require greater weight loss than their counterparts with atypical AN of the same sex, age, height and premorbid weight. Indeed, patients with atypical AN had a 5.6 kg lower weight loss after controlling for these variables. In comparison to the reference population, we found a lower and higher mean premorbid weight in patients with AN and atypical AN, respectively. Considering previous psychological and medical comparisons showing little differences between AN and atypical AN, we view a single disorder as the most parsimonious explanation. Etiological models need to particularly account for the strong relationship between weight loss and premorbid body weight.

Development and validation of a rule-based algorithm to identify periodontal diagnosis using structured electronic health record data.

AIM: To develop and validate an automated electronic health record (EHR)-based algorithm to suggest a periodontal diagnosis based on the 2017 World Workshop on the Classification of Periodontal Diseases and Conditions. MATERIALS AND METHODS: Using material published from the 2017 World Workshop, a tool was iteratively developed to suggest a periodontal diagnosis based on clinical data within the EHR. Pertinent clinical data included clinical attachment level (CAL), gingival margin to cemento-enamel junction distance, probing depth, furcation involvement (if present) and mobility. Chart reviews were conducted to confirm the algorithm's ability to accurately extract clinical data from the EHR, and then to test its ability to suggest an accurate diagnosis. Subsequently, refinements were made to address limitations of the data and specific clinical situations. Each refinement was evaluated through chart reviews by expert periodontists at the study sites. RESULTS: Three-hundred and twenty-three charts were manually reviewed, and a periodontal diagnosis (healthy, gingivitis or periodontitis including stage and grade) was made by expert periodontists for each case. After developing the initial version of the algorithm using the unmodified 2017 World Workshop criteria, accuracy was 71.8% for stage alone and 64.7% for stage and grade. Subsequently, 16 modifications to the algorithm were proposed and 14 were accepted. This refined version of the algorithm had 79.6% accuracy for stage alone and 68.8% for stage and grade together. CONCLUSIONS: Our findings suggest that a rule-based algorithm for suggesting a periodontal diagnosis using EHR data can be implemented with moderate accuracy in support of chairside clinical diagnostic decision making, especially for inexperienced clinicians. Grey-zone cases still exist, where clinical judgement will be required. Future applications of similar algorithms with improved performance will depend upon the quality (completeness/accuracy) of EHR data.

Combining public datasets for automated tooth assessment in panoramic radiographs.

OBJECTIVE: Panoramic radiographs (PRs) provide a comprehensive view of the oral and maxillofacial region and are used routinely to assess dental and osseous pathologies. Artificial intelligence (AI) can be used to improve the diagnostic accuracy of PRs compared to bitewings and periapical radiographs. This study aimed to evaluate the advantages and challenges of using publicly available datasets in dental AI research, focusing on solving the novel task of predicting tooth segmentations, FDI numbers, and tooth diagnoses, simultaneously. MATERIALS AND METHODS: Datasets from the OdontoAI platform (tooth instance segmentations) and the DENTEX challenge (tooth bounding boxes with associated diagnoses) were combined to develop a two-stage AI model. The first stage implemented tooth instance segmentation with FDI numbering and extracted regions of interest around each tooth segmentation, whereafter the second stage implemented multi-label classification to detect dental caries, impacted teeth, and periapical lesions in PRs. The performance of the automated tooth segmentation algorithm was evaluated using a free-response receiver-operating-characteristics (FROC) curve and mean average precision (mAP) metrics. The diagnostic accuracy of detection and classification of dental pathology was evaluated with ROC curves and F1 and AUC metrics. RESULTS: The two-stage AI model achieved high accuracy in tooth segmentations with a FROC score of 0.988 and a mAP of 0.848. High accuracy was also achieved in the diagnostic classification of impacted teeth (F1 = 0.901, AUC = 0.996), whereas moderate accuracy was achieved in the diagnostic classification of deep caries (F1 = 0.683, AUC = 0.960), early caries (F1 = 0.662, AUC = 0.881), and periapical lesions (F1 = 0.603, AUC = 0.974). The model's performance correlated positively with the quality of annotations in the used public datasets. Selected samples from the DENTEX dataset revealed cases of missing (false-negative) and incorrect (false-positive) diagnoses, which negatively influenced the performance of the AI model. CONCLUSIONS: The use and pooling of public datasets in dental AI research can significantly accelerate the development of new AI models and enable fast exploration of novel tasks. However, standardized quality assurance is essential before using the datasets to ensure reliable outcomes and limit potential biases.

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.

BACKGROUND: The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS: Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS: A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (ß = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS: Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

What to do about atypical anorexia nervosa? Commentary on Walsh et al. (2023).

Changes made to the DSM Eating Disorders over the years have aimed to reduce the prevalence of the residual DSM Eating Disorder categories (e.g., Other Specified Eating Disorder). Atypical Anorexia Nervosa (AN), included since DSM-IV as an example of a presentation not meeting criteria for a specific eating disorder, appears to be more prevalent than AN. It is defined as meeting all of the criteria for AN except that, after significant weight loss, weight is at or above normal. As suggested by the Walsh et al. review, lack of definitional precision will likely complicate efforts to determine whether atypical AN is best considered a variant of AN or a distinct category. Problems with the current definition of atypical AN include (1) a lack of precision regarding what constitutes "significant" weight loss; (2) whether the weight loss can occur at any point in the individual's lifetime; and (3) whether there an upper limit to weight being above normal. It is suggested that researchers develop consensus diagnostic criteria and assessment tools to facilitate the collection of empirical data about atypical AN in order to lay the groundwork for future decisions about its nosological status.

Examining orthorexia nervosa: Using latent profile analysis to explore potential diagnostic classification and subtypes in a non-clinical sample.

Orthorexia nervosa (ON) is a proposed psychological disorder characterized by a pathological preoccupation with healthy eating. The purpose of the current study was to clarify the relationships between ON and related forms of psychopathology. In addition, we sought to explore whether there may be subtypes of ON and if ON is associated with BMI, gender, or social media use. The sample included 333 undergraduate students (72% female, Mage = 20.91) who completed measures of ON, eating disorder (ED) symptoms, obsessive compulsive disorder (OCD), obsessive compulsive personality disorder (OCPD), and health anxiety. Latent profile analysis detected three distinct groups with high ON scores. The ON/ED combined group (n = 16) was characterized by high levels of psychopathology, particularly in the areas of ON and eating disorder symptoms. In comparison, the ON/ED combined, without weight/shape concerns group (n = 35) had fewer body-related concerns. The ON only group (n = 23) reported minimal ED pathology. Regression analyses revealed those in the ON only group were more likely to be male, while the ON/ED group was associated with higher BMI. Being in the ON/ED combined, without weight/shape concerns was associated with viewing and sharing healthy eating content on social media. Our findings suggest that ON has the most overlap with ED pathology as compared to OCD, OCPD, and health anxiety, and that there may be three subtypes of ON. The first two share significant overlap with ED symptomatology while the third appears relatively distinct, characterized by less disordered eating and fewer positive emotions related to healthy eating. Future research should examine these subtypes more closely to determine whether they are clinically meaningful, potentially requiring different interventions.

A Tutorial on Cognitive Diagnosis Modeling for Characterizing Mental Health Symptom Profiles Using Existing Item Responses.

In research applications, mental health problems such as alcohol-related problems and depression are commonly assessed and evaluated using scale scores or latent trait scores derived from factor analysis or item response theory models. This tutorial paper demonstrates the use of cognitive diagnosis models (CDMs) as an alternative approach to characterizing mental health problems of young adults when item-level data are available. Existing measurement approaches focus on estimating the general severity of a given mental health problem at the scale level as a unidimensional construct without accounting for other symptoms of related mental health problems. The prevailing approaches may ignore clinically meaningful presentations of related symptoms at the item level. The current study illustrates CDMs using item-level data from college students (40 items from 719 respondents; 34.6% men, 83.9% White, and 16.3% first-year students). Specifically, we evaluated the constellation of four postulated domains (i.e., alcohol-related problems, anxiety, hostility, and depression) as a set of attribute profiles using CDMs. After accounting for the impact of each attribute (i.e., postulated domain) on the estimates of attribute profiles, the results demonstrated that when items or attributes have limited information, CDMs can utilize item-level information in the associated attributes to generate potentially meaningful estimates and profiles, compared to analyzing each attribute independently. We introduce a novel visual inspection aid, the lens plot, for quantifying this gain. CDMs may be a useful analytical tool to capture respondents' risk and resilience for prevention research.

Modification Indices for Diagnostic Classification Models.

Diagnostic classification models (DCMs) are psychometric models for evaluating a student's mastery of the essential skills in a content domain based upon their responses to a set of test items. Currently, diagnostic model and/or Q-matrix misspecification is a known problem with limited avenues for remediation. To address this problem, this paper defines a one-sided score statistic that is a computationally efficient method for detecting under-specification at the item level of both the Q-matrix and the model parameters of the particular DCM chosen in an analysis. This method is analogous to the modification indices widely used in structural equation modeling. The results of a simulation study show the Type I error rate of modification indices for DCMs are acceptably close to the nominal significance level when the appropriate mixture χ2 reference distribution is used. The simulation results indicate that modification indices are very powerful in the detection of an under-specified Q-matrix and have ample power to detect the omission of model parameters in large samples or when the items are highly discriminating. An application of modification indices for DCMs to an analysis of response data from a large-scale administration of a diagnostic test demonstrates how they can be useful in diagnostic model refinement.

Improving reliability estimation in cognitive diagnosis modeling.

Cognitive diagnosis models (CDMs) are used in educational, clinical, or personnel selection settings to classify respondents with respect to discrete attributes, identifying strengths and needs, and thus allowing to provide tailored training/treatment. As in any assessment, an accurate reliability estimation is crucial for valid score interpretations. In this sense, most CDM reliability indices are based on the posterior probabilities of the estimated attribute profiles. These posteriors are traditionally computed using point estimates for the model parameters as approximations to their populational values. If the uncertainty around these parameters is unaccounted for, the posteriors may be overly peaked, deriving into overestimated reliabilities. This article presents a multiple imputation (MI) procedure to integrate out the model parameters in the estimation of the posterior distributions, thus correcting the reliability estimation. A simulation study was conducted to compare the MI procedure with the traditional reliability estimation. Five factors were manipulated: the attribute structure, the CDM model (DINA and G-DINA), test length, sample size, and item quality. Additionally, an illustration using the Examination for the Certificate of Proficiency in English data was analyzed. The effect of sample size was studied by sampling subsets of subjects from the complete data. In both studies, the traditional reliability estimation systematically provided overestimated reliabilities, whereas the MI procedure offered more accurate results. Accordingly, practitioners in small educational or clinical settings should be aware that the reliability estimation using model parameter point estimates may be positively biased. R codes for the MI procedure are made available.

Nonparametric cognitive diagnosis of profiles of mathematical knowledge of teacher education candidates.

Traditionally, the selection process of teacher candidates has emphasized the assessment of subject matter and pedagogical knowledge using psychometric methodologies, which simply organize candidates in continuous scales and require a large number of samples. However, these methods do not allow for the identification of candidates' knowledge profiles and learning paths, which would help develop programs tailored to support students in their training process. In this study, an evaluation instrument was developed by using the nonparametric approach to model diagnostic classifications and was then validated on a sample of 119 participants. This instrument allows for disaggregating candidates' initial knowledge and establishing relationships between its components. The results showed that candidates present a variety of profiles, which may consider more than one attribute. Not only does it provide a score that can be used for selection processes, it also provides useful information for initial teacher training methods.

A diagnostic classification model of college instructors' value beliefs towards open educational resources.

Open educational resources (OER) can be cost-effective alternatives to traditional textbooks for higher education faculty to decrease student spending on textbooks. To further advocate college instructors' use of OER, understanding their value belief towards integrating OER in teaching is necessary but currently absent. This study thus analyzed 513 college instructors' value beliefs about using OER in college teaching by applying a psychometric model known as diagnostic classification models (DCMs). The findings of this study validated the three constructs in value beliefs measured by an OER user survey: engaging students, customizing classroom materials and supporting personal professional development. The results showed that a considerable number of college instructors maintained a low level of value beliefs towards using OER. We further provided individualized classification for each college instructor in terms of the three types of value beliefs. In addition, this study investigated how pre-determined latent classes of value beliefs influenced college instructors' practice and perception of using OER. Particularly, college instructors who value OER to address their profession needs are more likely to adapt OER in their teaching rather than merely reusing existing copies. Practical implications of supporting higher education faculty's use of OER are discussed in the end.

Structural Parameter Standard Error Estimation Method in Diagnostic Classification Models: Estimation and Application.

The information matrix or its inverse variance-covariance matrix for the maximum likelihood estimates of model parameters in diagnostic classification models plays a key role in statistical inference. Although both the item and structural parameters should be contained in the calculation of the information matrix simultaneously, previous studies have mainly focused on performance of the item parameter standard error (SE), no study has investigated the structural parameter SE estimation methods systematically. In this study, we propose a class of structural parameter SE estimation methods based on the empirical cross-product matrix, the observed information matrix, and the sandwich-type covariance matrix. A simulation study was conducted under different attribute hierarchy structures, the findings suggest that the proposed methods are useful for empirical researchers and practitioners in evaluating the variability of structural parameter estimators. We illustrate the application of the structural parameter SE estimation methods for exploring the presence of an attribute hierarchy using real data.

Cytomolecular Classification of Thyroid Nodules Using Fine-Needle Washes Aspiration Biopsies.

Fine-needle aspiration biopsies (FNA) represent the gold standard to exclude the malignant nature of thyroid nodules. After cytomorphology, 20-30% of cases are deemed "indeterminate for malignancy" and undergo surgery. However, after thyroidectomy, 70-80% of these nodules are benign. The identification of tools for improving FNA's diagnostic performances is explored by matrix-assisted laser-desorption ionization mass spectrometry imaging (MALDI-MSI). A clinical study was conducted in order to build a classification model for the characterization of thyroid nodules on a large cohort of 240 samples, showing that MALDI-MSI can be effective in separating areas with benign/malignant cells. The model had optimal performances in the internal validation set (n = 70), with 100.0% (95% CI = 83.2-100.0%) sensitivity and 96.0% (95% CI = 86.3-99.5%) specificity. The external validation (n = 170) showed a specificity of 82.9% (95% CI = 74.3-89.5%) and a sensitivity of 43.1% (95% CI = 30.9-56.0%). The performance of the model was hampered in the presence of poor and/or noisy spectra. Consequently, restricting the evaluation to the subset of FNAs with adequate cellularity, sensitivity improved up to 76.5% (95% CI = 58.8-89.3). Results also suggest the putative role of MALDI-MSI in routine clinical triage, with a three levels diagnostic classification that accounts for an indeterminate gray zone of nodules requiring a strict follow-up.

Diagnosing cognitive disorders in older adults with epilepsy.

OBJECTIVE: To characterize the nature and prevalence of cognitive disorders in older adults with temporal lobe epilepsy (TLE) and compare their cognitive profiles to patients with amnestic mild cognitive impairment (ie, aMCI). METHODS: Seventy-one older patients with TLE, 77 aMCI, and 69 normal aging controls (NACs), all 55-80 years of age, completed neuropsychological measures of memory, language, executive function, and processing speed. An actuarial neuropsychological method designed to diagnose MCI was applied to individual patients to identify older adults with TLE who met diagnostic criteria for MCI (TLE-MCI). A linear classifier was performed to evaluate how well the diagnostic criteria differentiated patients with TLE-MCI from aMCI. In TLE, the contribution of epilepsy-related and vascular risk factors to cognitive impairment was evaluated using multiple regression. RESULTS: Forty-three TLE patients (60%) met criteria for TLE-MCI, demonstrating marked deficits in both memory and language. When patients were analyzed according to age at seizure onset, 63% of those with an early onset (<50 years) versus 56% of those with late onset (≥ 50 years) met criteria for TLE-MCI. A classification model between TLE-MCI and aMCI correctly classified 81.1% (90.6% specificity, 61.3% sensitivity) of the cohort based on neuropsychological scores. Whereas TLE-MCI showed greater deficits in language relative to aMCI, patients with aMCI showed greater rapid forgetting on memory measures. Both epilepsy-related risk factors and the presence of leukoaraiosis on MRI contributed to impairment profiles in TLE-MCI. SIGNIFICANCE: Cognitive impairment is a common comorbidity in epilepsy and it presents in a substantial number of older adults with TLE. Although the underlying etiologies are unknown in many patients, the TLE-MCI phenotype may be secondary to an accumulation of epilepsy and vascular risk factors, signal the onset of a neurodegenerative disease, or represent a combination of factors.

Does three-grade classification of T2-weighted increased signal intensity reflect the severity of myelopathy and surgical outcomes in patients with cervical compressive myelopathy? A systematic review and meta-analysis.

The three-grade classification of increased signal intensity (ISI) on T2-weighted magnetic resonance imaging (MRI) is used extensively in patients with cervical compressive myelopathy (CCM). However, the efficacy and value in the prediction of this classification are still unclear and no systematic review and meta-analysis have been conducted on this topic. The objective of this study is to investigate the efficacy and value in prediction of the three-grade classification of ISI on the severity of myelopathy and surgical outcomes. Randomized or non-randomized controlled studies using three-grade classification of ISI (grade 0, none; grade 1, light or obscure; and grade 2, intense or bright) in patients with CCM were sought in the following databases: PubMed, Embase, and Cochrane Library. The pooled Japanese Orthopedic Association (JOA)/modified JOA (mJOA) score, neuro-functional recovery rate, C2-C7 lordotic angle, and range of motion (ROM) were calculated. A total of 8 studies containing 1101 patients were included in this review. Patients in grade 0 had the highest preoperative and postoperative JOA/mJOA score and recovery rate, while those parameters for patients in grade 2 were the lowest. Nevertheless, no statistically significant difference was found regarding the preoperative C2-C7 lordotic angle and ROM among three grades. Our meta-analysis suggests that the three-grade classification of ISI on T2-weighted MRI can reflect the severity of myelopathy and surgical outcomes in patients with CCM. The higher ISI grade indicates more severe myelopathy and surgical outcomes. Overall, the three-grade classification of ISI is instructive and should be used universally.

Constructing classification boundaries in the memory clinic: negotiating risk and uncertainty in constituting mild cognitive impairment.

Determining the boundaries around processes of 'normal' ageing and pathological cognitive deterioration associated with Alzheimer's disease (AD) is a difficult process, complicated further by the expansion of the disease category to include mild cognitive impairment (MCI). MCI is a label used to identify individuals with the symptoms of cognitive deterioration not attributable to 'normal ageing' but deemed to be 'at risk' of developing AD despite clinical uncertainty around whether individuals will go on to develop the condition in the future. Drawing on qualitative data gathered across an out-patient memory service, this article examines practitioners' accounts of the complexity associated with constructing the boundaries around MCI, AD and age in the clinic. Practitioners utilise uncertainty by classifying patients with MCI to keep them on for review to account for the possibility that patients may go on to develop AD but they also recognise the difficulty in predicting future progression to AD. Negotiating classification boundaries in the clinic is, however, not only about managing uncertainty regarding potential future risk but also about navigating the wider social and political context in which ageing and cognitive deterioration intersect, and are constructed and managed.

Using Diagnostic Classification Models to Validate Attribute Hierarchies and Evaluate Model Fit in Bayesian Networks.

We investigate the relationship between Bayesian inference networks (BayesNets) and diagnostic classification models (DCMs). Specifically, we demonstrate and empirically examine the equivalency of parameterizations between BayesNets and DCMs. Then, we propose a model-comparison framework for testing the model fit of BayesNets, in which we show how BayesNets are nested within the saturated DCM structural models. Additionally, we show when attributes feature a linear hierarchy, the Hierarchical DCM is nested within both BayesNets and saturated DCMs. The usefulness of proposed framework and model-fit testing strategy was supported by the results of analyzing both simulated and empirical data.