Quantization of Midisuperspace Models

We give a comprehensive review of the quantization of midisuperspace models. Though the main focus of the paper is on quantum aspects, we also provide an introduction to several classical points related to the definition of these models. We cover some important issues, in particular, the use of the principle of symmetric criticality as a very useful tool to obtain the required Hamiltonian formulations. Two main types of reductions are discussed: those involving metrics with two Killing vector fields and spherically symmetric models. We also review the more general models obtained by coupling matter fields to these systems. Throughout the paper we give separate discussions for standard quantizations using geometrodynamical variables and those relying on loop quantum gravity inspired methods.Comment: To appear in Living Review in Relativit

Reasons for accepting or declining to participate in randomized clinical trials for cancer therapy

This paper reports on the reasons why patients agreed to or declined entry into randomized trials of cancer following discussions conducted by clinicians in both District General and University Hospitals. Two hundred and four patients completed a 16-item questionnaire following the consultation, of these 112 (55%) were women with breast cancer. Overall results showed that 147 (72.1%) patients accepted entry to a randomized clinical trial (RCT). The main reasons nominated for participating in a trial were that ‘others will benefit’ (23.1%) and ‘trust in the doctor’ (21.1%). One of the main reasons for declining trial entry was that patients were ‘worried about randomization’ (19.6%). There was a significantly higher acceptance rate for trials providing active treatment in every arm 98 (80.6%) compared with those trials with a no treatment arm 46 (60.5%), χ2test P = 0.003. The study outlines a number of factors that appear to influence a patient’s decision to accept or decline entry into an RCT of cancer therapy. An important factor is whether or not the trial offers active treatment in all arms of the study. Communication that promotes trust and confidence in the doctor is also a powerful motivating influence. © 2000 Cancer Research Campaig

A randomised controlled trial of nurse-managed trial conclusion following early phase cancer trial participation

The effect of a nurse-managed intervention, for early phase cancer trial participants at trial conclusion, on psychosocial outcomes was evaluated at two cancer centres in the Midlands, England using a randomised controlled trial. It involved 117 patients who were participating in an early phase cancer clinical trial. It was a nurse-managed trial exit, which included a trial exit interview, trial feedback information leaflet and telephone follow-up compared with standard care at trial conclusion. Psychological distress at 1 week and 4–6 weeks post-trial conclusion, patient's knowledge and understanding and patient's satisfaction were assessed. The results showed there was no significant difference between the two groups regarding scores for anxiety and depression at time one and time two. There is some suggestion that the intervention reduced anxiety from trial conclusion to follow-up (P=0.27). Patients in both groups felt they had contributed to cancer research through trial participation. However, intervention patients were more likely to feel that they knew how the trial was going (P<0.001), knew how other people in the trial were doing (P=0.001), had all the feedback they needed about the trial they took part in (P<0.01) and knew how they would be followed up (P=0.02). Patient satisfaction with the intervention was high (median score=4.5 where 5 is greatest satisfaction). In conclusion, nurse-managed trial conclusion led to positive outcomes for patients who had recently completed a clinical trial

Quality of life data as prognostic indicators of survival in cancer patients: an overview of the literature from 1982 to 2008

<p>Abstract</p> <p>Background</p> <p>Health-related quality of life and survival are two important outcome measures in cancer research and practice. The aim of this paper is to examine the relationship between quality of life data and survival time in cancer patients.</p> <p>Methods</p> <p>A review was undertaken of all the full publications in the English language biomedical journals between 1982 and 2008. The search was limited to cancer, and included the combination of keywords 'quality of life', 'patient reported-outcomes' 'prognostic', 'predictor', 'predictive' and 'survival' that appeared in the titles of the publications. In addition, each study was examined to ensure that it used multivariate analysis. Purely psychological studies were excluded. A manual search was also performed to include additional papers of potential interest.</p> <p>Results</p> <p>A total of 451 citations were identified in this rapid and systematic review of the literature. Of these, 104 citations on the relationship between quality of life and survival were found to be relevant and were further examined. The findings are summarized under different headings: heterogeneous samples of cancer patients, lung cancer, breast cancer, gastro-oesophageal cancers, colorectal cancer, head and neck cancer, melanoma and other cancers. With few exceptions, the findings showed that quality of life data or some aspects of quality of life measures were significant independent predictors of survival duration. Global quality of life, functioning domains and symptom scores - such as appetite loss, fatigue and pain - were the most important indicators, individually or in combination, for predicting survival times in cancer patients after adjusting for one or more demographic and known clinical prognostic factors.</p> <p>Conclusion</p> <p>This review provides evidence for a positive relationship between quality of life data or some quality of life measures and the survival duration of cancer patients. Pre-treatment (baseline) quality of life data appeared to provide the most reliable information for helping clinicians to establish prognostic criteria for treating their cancer patients. It is recommended that future studies should use valid instruments, apply sound methodological approaches and adequate multivariate statistical analyses adjusted for socio-demographic characteristics and known clinical prognostic factors with a satisfactory validation strategy. This strategy is likely to yield more accurate and specific quality of life-related prognostic variables for specific cancers.</p