No abstract

The data provide evidence that the combination of medical treatment plus multicomponent behavioral treatment is superior to medical treatment alone in the therapy of IBS.

This study employed the Pain Beliefs and Perceptions Inventory (PBPAI) (Williams and Thorn 1989) with a German sample (n = 193) of pain patients. The original version has 3 subscales: (1) self-blame (S-B), (2) perception of pain as mysterious (MYST), and (3) beliefs about the temporal stability of pain (TIME). Item statistics, factor structure, and discriminant validity are reported. Factor analysis favored a 4-factor structure and replicated a finding by Strong et al. (1992). The TIME scale can be subdivided into 2 subscales: beliefs that pain is a constant and enduring experience ("Constancy"), and beliefs about the long-term chronicity of pain ("Acceptance"). Constancy showed higher correlations with self-reported psychological symptomatology (anxiety, general physical troubles, pain intensity) than did Acceptance, MYST, and S-B.

Based on existing models for pain chronicity and effective treatment strategies for patients with chronic low back pain, a multidisciplinary rehabilitation programme for an outpatient group setting was developed. The main treatment components address the patient's physical functional capacity (functional restoring), cognitive and affective processes (pain management strategies), and behavioural and ergonomical aspects (back school elements). Short-term (immediately after intervention) and long-term effects (at 6-months follow-up) of the intervention were assessed in a randomized controlled study. Dependent variables were pain measures, functional capacity, disability, muscular strength and endurance, pain and posture-related self-efficacy, attitudes, depression, well-being, behavioural habits and posture assessed by a standardized behavioural observation method. Immediately after the intervention, patients in the treatment group (n=36) showed significant improvement over patients in the control group (n=29) in all variables except depression and muscular strength and endurance. At 6-months follow-up, compared to pretreatment scores, patients continued to show beneficial effects in pain intensity and frequency, posture, posture-related self-efficacy and well-being. In contrast to post-treatment results, there were also significant improvements in strength and endurance. Overall results testify to the effectiveness of the intervention programme. Future studies (with larger sample sizes) should aim at a further improvement of functional capacity and disability perception, an analysis of differential treatment effects, and strategies for an improved long-term maintenance of the changes induced by the programme.

Purpose To develop and validate a decision support tool for mammographic mass lesions based on a standardized descriptor terminology (BI-RADS lexicon) to reduce variability of practice. Materials and Methods We used separate training data (1276 lesions, 138 malignant) and validation data (1177 lesions, 175 malignant). We created naïve Bayes (NB) classifiers from the training data with 10-fold cross validation. Our “inclusive model” comprised BI-RADS categories, BI-RADS descriptors and age as predictive variables, our “descriptor model” comprised BI-RADS descriptors and age. The resulting NB classifiers were applied to the validation data. We evaluated and compared classifier performance with ROC-analysis. Results In the training data, the inclusive model yields an AUC of 0.959, the descriptor model yields an AUC of 0.910 (P<0.001). The inclusive model is superior to the clinical performance (BI-RADS categories alone, P<0.001), the descriptor model performs similarly. When applied to the validation data, the inclusive model yields an AUC of 0.935, the descriptor model yields an AUC of 0.876 (P<0.001). Again, the inclusive model is superior to the clinical performance (P<0.001), the descriptor model performs similarly. Conclusion We consider our classifier a step towards a more uniform interpretation of combinations of BI-RADS descriptors. We provide our classifier at www.ebm-radiology.com/nbmm/index.html.