Search

Background: It is well established that trigeminal neuralgia is more prevalent in females than in males. Neurovascular compression with morphological changes of the trigeminal root represents the most recognized etiological factor. However, other factors may play a role in the framework of a multi-hit model. The primary aim of this study was to investigate sex differences in radiological and clinical characteristics of trigeminal neuralgia to better understand the multifactorial origin of this peculiar neuropathic pain condition. Methods: In this cross-sectional study patients with a definite diagnosis of primary trigeminal neuralgia were consecutively enrolled. Each patient underwent 3T MRI with sequences dedicated to the study of neurovascular compression. Major morphological changes of the trigeminal root were quantitatively assessed. Clinical characteristics were systematically collected through a dedicated questionnaire. A logistic regression model was implemented to predict radiological and clinical characteristics based on sex. Results: A total of 114 patients with classical (87) or idiopathic trigeminal neuralgia (27) were enrolled. Female sex was predictive for idiopathic trigeminal neuralgia. Male sex was predictive, among the comorbidities and clinical characteristics, for hypertension, the involvement of the left side and the second trigeminal division, alone or with the ophthalmic division. Discussion: The preponderance of TN in the female sex and the association between idiopathic TN and the female sex suggest the role of additional etiological factors in the framework of a multi-hit model. The identification of clinical variables predicted by sex suggests the possibility that distinct phenotypes, with peculiar pathophysiological and therapeutic aspects, may occur in females and males. [ABSTRACT FROM AUTHOR]

The Mallows model (MM) occupies a central role in parametric modelling of ranking data to learn preferences of a population of judges. Despite the wide range of metrics for rankings that can be considered in the model specification, the choice is typically limited to the Kendall, Cayley or Hamming distances, due to the closed-form expression of the related model normalizing constant. This work instead focuses on the Mallows model with Spearman distance (MMS). A novel approximation of the normalizing constant is introduced to allow inference even with a large number of items. This allows us to develop and implement an efficient and accurate EM algorithm for estimating finite mixtures of MMS aimed at i) enlarging the applicability to samples drawn from heterogeneous populations, and ii) dealing with partial rankings affected by diverse forms of censoring. These novelties encompass the critical inferential steps that traditionally limited the use of this distance in practice, and render the MMS comparable (or even preferable) to the MMs with other metrics in terms of computational burden. The inferential ability of the EM scheme and the effectiveness of the approximation are assessed by extensive simulation studies. Finally, we show that the application to three real-world datasets endorses our proposals also in the comparison with competing mixtures of ranking models. [ABSTRACT FROM AUTHOR]

Multistage ranking models, including the popular Plackett–Luce distribution (PL), rely on the assumption that the ranking process is performed sequentially, by assigning the positions from the top to the bottom one (forward order). A recent contribution to the ranking literature relaxed this assumption with the addition of the discrete-valued reference order parameter, yielding the novel Extended Plackett–Luce model (EPL). Inference on the EPL and its generalization into a finite mixture framework was originally addressed from the frequentist perspective. In this work, we propose the Bayesian estimation of the EPL in order to address more directly and efficiently the inference on the additional discrete-valued parameter and the assessment of its estimation uncertainty, possibly uncovering potential idiosyncratic drivers in the formation of preferences. We overcome initial difficulties in employing a standard Gibbs sampling strategy to approximate the posterior distribution of the EPL by combining the data augmentation procedure and the conjugacy of the Gamma prior distribution with a tuned joint Metropolis–Hastings algorithm within Gibbs. The effectiveness and usefulness of the proposal is illustrated with applications to simulated and real datasets. [ABSTRACT FROM AUTHOR]

The elicitation of an ordinal judgment on multiple alternatives is often required in many psychological and behavioral experiments to investigate preference/choice orientation of a specific population. The Plackett-Luce model is one of the most popular and frequently applied parametric distributions to analyze rankings of a finite set of items. The present work introduces a Bayesian finite mixture of Plackett-Luce models to account for unobserved sample heterogeneity of partially ranked data. We describe an efficient way to incorporate the latent group structure in the data augmentation approach and the derivation of existing maximum likelihood procedures as special instances of the proposed Bayesian method. Inference can be conducted with the combination of the Expectation-Maximization algorithm for maximum a posteriori estimation and the Gibbs sampling iterative procedure. We additionally investigate several Bayesian criteria for selecting the optimal mixture configuration and describe diagnostic tools for assessing the fitness of ranking distributions conditionally and unconditionally on the number of ranked items. The utility of the novel Bayesian parametric Plackett-Luce mixture for characterizing sample heterogeneity is illustrated with several applications to simulated and real preference ranked data. We compare our method with the frequentist approach and a Bayesian nonparametric mixture model both assuming the Plackett-Luce model as a mixture component. Our analysis on real datasets reveals the importance of an accurate diagnostic check for an appropriate in-depth understanding of the heterogenous nature of the partial ranking data. [ABSTRACT FROM AUTHOR]

To propose a clinical prognostic index for death and heart failure in patients with ischemic cardiomyopathy implanted with an ICD. This prospective study included 192 consecutive patients (age 68 ± 10) recruited from 2004 to 2009 and implanted with an ICD for MADIT II criteria. All patients performed 24-h ambulatory blood pressure monitoring after discharge and common haematological samples. The prognostic index (PI) was built according to the formula: 120 − age + mean 24 h systolic blood pressure − (creatinine × 10). Other variables were assessed: EF, haemoglobin concentration, mean 24 h heart rate and diastolic blood pressure, sodium level, pacing mode and diabetes. Non-arrhythmic cardiac death and new hospitalizations for heart failure during 1-year follow-up were the combined end point. A total of 48 events (25 %) occurred during the follow-up: 7 cardiac deaths and 41 hospitalizations for acute heart failure. Cox proportional-hazards model showed that PI was the only predictor of events (HR = 0.96; CI 95 % 0.944-0.976, p < 0.0001). ROC curve showed that PI best cut-off was 144, with AUC 0.79, p < 0.0001; sensitivity 77 %, specificity 74 %, positive predictive value 50 %, negative predictive value 90 %. PI was predictive of events in a clinical setting where EF had no predictive value. PI works according to the rule 'the lower the worse'. The high negative predictive value (90 %) of PI allows to identify subjects at lower risk for death and heart failure. PI can be a practical tool to stratify risk in ischemic cardiomyopathy. [ABSTRACT FROM AUTHOR]