A seven-year follow-up of men following a benign prostate biopsy

Objectives: To determine the incidence and clinical relevance of newly diagnosed cases of prostate cancer in a group of men who had an elevated PSA and benign prostate biopsy 7 years previously. Patients and Method: Patients under the age of 80 years with an elevated PSA who had had a benign prostate biopsy in the 12 months between March 1, 1994 and February 28, 1995 were studied. One hundred and sixty four patients with a mean age of 66.8 years (range 47-79 years) were identified. The mean PSA for this group was 10.3 ng/ml (range 4.1-81 ng/ml). One hundred and fifty nine of the 164 (97%) hospital records were available for review and all but 21 (12.8%) of the General Practitioners were contacted. Results: Eighteen (11%) of the original 164 patients were subsequently diagnosed with prostate cancer, 2 died from their disease. Conclusions: In a population where the follow-up of patients with a benign biopsy was arranged on clinical grounds alone, 11% of the study group were diagnosed with prostate cancer during a seven-year follow-up. Although some of these cancers appear to be slow growing, most of those diagnosed in the initial follow-up period were deemed to be clinically significant and a small proportion progressed rapidly to metastases. All patients who have an elevated PSA, but benign biopsy, should undergo a period of PSA monitoring until it is clear that their PSA is not rising. We propose an initial intensive monitoring period to avoid missing those with clinically aggressive disease. (C) 2003 Elsevier Science B.V. All rights reserved.

A follow-up study of characteristics of young people that dropout and continue psychotherapy: service implications for a clinic in the community

Background: The paper reports the findings from a follow-up study of the factors that contribute to whether young people dropout or continue once-weekly psychotherapy at a voluntary sector psychotherapy service for young people aged 12 to 21 years. Method: The study uses data from an ongoing audit of the psychotherapy service that started in 1993; 882 young people were included in the study. Premature termination of treatment was defined as dropping out before the 21st session. Continuation in treatment was defined as remaining in therapy after 20 sessions. Measures and areas of interest used in the study include diagnostic measures, the Youth Self Report Form and Young Adult Self Report Form, demographic characteristics and treatment related information. Results: Young people who continued in treatment were more likely to be older, have anxieties about sexual and relationship issues and have higher scores on self-reported anxiety-depression. Young people who dropped out of treatment were more likely to be younger, have higher self-reported delinquency scores, have a diagnosis of hyperactivity-conduct disorder and be homeless. Conclusions: The study of treatment termination has demonstrated the value of service audit and has led to a significant change in clinical practice.

Cognitive follow up of a small cohort of Huntington's disease patients over a 5 year period

A small group of patients with manifest Huntington's disease (HD) were followed longitudinally to assess cognitive decline in relation to time from disease diagnosis. This article looks at performance on a range of computerised and pencil and paper cognitive tasks in patients 5 years post diagnosis, who were assessed annually for a 5 year follow up period. The almost universal cognitive decline reported in other longitudinal studies of HD was not replicated in this study. It was proposed that longitudinal follow up in HD is complicated by the varying degree to which different tasks are able to withstand repeated administration; a finding which would have significant implications on study design in future trials of cognitive enhansing interventions.

The role of temperament and family environment in the development of anxiety disorder: two-year follow-up

Objective Behavioural inhibition (BI) in early childhood is associated with increased risk for anxiety. The present research examines BI alongside family environment factors, specifically maternal negativity and overinvolvement, maternal anxiety and mother-child attachment, with a view to providing a broader understanding of the development of child anxiety. Method Participants were 202 children classified at age 4 as either behaviourally inhibited (N=102) or uninhibited (N=100). Family environment, BI and child anxiety were assessed at baseline and child anxiety and BI were assessed again two-years later when participants were aged 6 years. Results After controlling for baseline anxiety, inhibited participants were significantly more likely to meet criteria for a diagnosis of social phobia and generalized anxiety disorder at follow-up. Path analysis suggested that maternal anxiety significantly affected child anxiety over time, even after controlling for the effects of BI and baseline anxiety. No significant paths from parenting or attachment to child anxiety were found. Maternal overinvolvement was significantly associated with BI at follow-up. Conclusions At age 4, BI, maternal anxiety and child anxiety represent risk factors for anxiety at age 6. Furthermore, overinvolved parenting increases risk for BI at age 6, which may then lead to the development of anxiety in later childhood.

P-Prism: a computationally efficient approach to scaling up classification rule induction

Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unseen data. Alternative algorithms have been developed such as the Prism algorithm. Prism constructs modular rules which produce qualitatively better rules than rules induced by TDIDT. However, along with the increasing size of databases, many existing rule learning algorithms have proved to be computational expensive on large datasets. To tackle the problem of scalability, parallel classification rule induction algorithms have been introduced. As TDIDT is the most popular classifier, even though there are strongly competitive alternative algorithms, most parallel approaches to inducing classification rules are based on TDIDT. In this paper we describe work on a distributed classifier that induces classification rules in a parallel manner based on Prism.

Towards a parallel computationally efficient approach to scaling up data stream classification

Advances in hardware technologies allow to capture and process data in real-time and the resulting high throughput data streams require novel data mining approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in real-time. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current classifiers for streaming data address this problem by using incremental learning algorithms. However, even so these algorithms are fast, they are challenged by high velocity data streams, where data instances are incoming at a fast rate. This is problematic if the applications desire that there is no or only a very little delay between changes in the patterns of the stream and absorption of these patterns by the classifier. Problems of scalability to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Neighbours (KNN) as the basis for a real-time adaptive and parallel methodology for scalable data stream classification tasks.