The PHES battery does not detect all cirrhotic patients with early neurological deficits, which are different in different patients.

BACKGROUND AND AIMS: The psychometric hepatic encephalopathy score (PHES) is the "gold standard" for minimal hepatic encephalopathy (MHE) diagnosis. Some reports suggest that some cirrhotic patients "without" MHE according to PHES show neurological deficits and other reports that neurological alterations are not homogeneous in all cirrhotic patients. This work aimed to assess whether: 1) a relevant proportion of cirrhotic patients show neurological deficits not detected by PHES; 2) cirrhotic patients with mild neurological deficits are a homogeneous population or may be classified in sub-groups according to specific deficits. METHODS: Cirrhotic patients "without" (n = 56) or "with" MHE (n = 41) according to PHES and controls (n = 52) performed psychometric tests assessing attention, concentration, mental processing speed, working memory and bimanual and visuomotor coordination. Heterogeneity of neurological alterations was analysed using Hierarchical Clustering Analysis. RESULTS: PHES classified as "with" MHE 42% of patients. Around 40% of patients "without" MHE according to PHES fail two psychometric tests. Oral SDMT, d2, bimanual and visuo-motor coordination tests are failed by 54, 51, 51 and 43% of patients, respectively. The earliest neurological alterations are different for different patients. Hierarchical clustering analysis shows that patients "without" MHE according to PHES may be classified in clusters according to the tests failed. In some patients coordination impairment appear before cognitive impairment while in others concentration and attention deficits appear before. CONCLUSIONS: PHES is not sensitive enough to detect early neurological alterations in a relevant proportion of cirrhotic patients. Oral SDMT, d2 and bimanual and visuo-motor coordination tests are more sensitive. The earliest neurological alterations are different in different cirrhotic patients. These data also have relevant clinical implications. Patients classified as "without MHE" by PHES belonging to clusters 3 and 4 in our study have a high risk of suffering clinical complications, including overt HE and must be diagnosed and clinically followed.

The use of neural networks for predicting the result of endoscopic treatment for vesico-ureteric reflux.

OBJECTIVE: To create an artificial neural network (ANN) to aid in predicting the results of endoscopic treatment for vesico-ureteric reflux (VUR). MATERIALS AND METHODS: During 1999-2001 we used endoscopic treatment in 261 ureteric units with VUR of all grades and causes. An ANN based on multilayer perceptron architecture was created using an 11 x 6 x 1 structure, taking the following as variables: the cause and grade of VUR, the patient's age and sex, the type of implanted substance and its volume, the number of treatments, the affected ureter, the endoscopic findings, and the type of cystography used. In all, 174 cases were used as training samples for the ANN and 87 to validate it. We calculated the sensitivity, specificity, positive (PPV) and negative predictive values (NPV), and the success rate (%) of the system. RESULTS: In the training group the ANN gave a sensitivity of 86.4%, a specificity of 89.5%, a PPV of 76% and NPV of 94%, with a success rate of 88.6%. In the same training group logistic regression (LR) gave respective values of 68.2%, 58.8%, 39%, 82.7% and 61.4%. In the validation group the respective values for the ANN were 71.4%, 81.6%, 58.8%, 88.6% and 78.9%, and in the same validation group the LR gave 64.4%, 50%, 32.1%, 79.2% and 53.9%. The Wilcoxon test confirmed the independence of both methods (P < 0.001). CONCLUSION: The ANN is an effective tool for assisting the urologist in indicating and applying endoscopic treatments for VUR.

ETAT: Expository Text Analysis Tool.

Qualitative methods that analyze the coherence of expository texts not only are time consuming, but also present challenges in collecting data on coding reliability. We describe software that analyzes expository texts more rapidly and produces a notable level of objectivity. ETAT (Expository Text Analysis Tool) analyzes the coherence of expository texts. ETAT adopts a symbolic representational system, known as conceptual graph structures. ETAT follows three steps: segmentation of a text into nodes, classification of the unidentified nodes, and linking the nodes with relational arcs. ETAT automatically constructs a graph in the form of nodes and their interrelationships, along with various attendant statistics and information about noninterrelated, isolated nodes. ETAT was developed in Java, so it is compatible with virtually all computer systems.

Disappearance of thalamic pain after parietal subcortical stroke.

A hypertensive man had a long standing history of contumacious hyperpathia in the right upper extremity, resistant to medical therapy, secondary to a lacunar infarct in the left thalamus. A second cerebrovascular accident caused a small lesion in the left corona radiata, interrupting the thalamoparietal interconnections, and terminated the pain instantly. Interruption of the subcortical parietal white matter may more effectively control pain than cortical lesions. A few surgeons have successfully treated rebellious chronic pain with stereotaxic operations in the corona radiata, resulting in lesions very similar to our patient's. This overlooked and nearly forgotten technique may still have value in treating selected cases.