The relationship between weight loss and interleukin 6 in non-small-cell lung cancer.

Markers of the inflammatory response, interleukin 6, C-reactive protein, albumin and full blood count, were measured in non-small-cell lung cancer (NSCLC) patients (n = 21) with and without weight loss ( > 5%). There were significant increases in circulating C-reactive protein (P < 0.001), interleukin 6 (P < 0.01) and platelets (P < 0.01) in the weight-losing group. Moreover, there was a statistically significant correlation (r = 0.785, P < 0.001) between interleukin 6 and C-reactive protein concentrations. These results are consistent with interleukin 6 and the acute phase response promoting weight loss in NSCLC

Chemotherapy versus supportive care in advanced non-small cell lung cancer: improved survival without detriment to quality of life

BACKGROUND: In 1995 a meta-analysis of randomised trials investigating the value of adding chemotherapy to primary treatment for non-small cell lung cancer (NSCLC) suggested a small survival benefit for cisplatin-based chemotherapy in each of the primary treatment settings. However, the metaanalysis included many small trials and trials with differing eligibility criteria and chemotherapy regimens. METHODS: The aim of the Big Lung Trial was to confirm the survival benefits seen in the meta-analysis and to assess quality of life and cost in the supportive care setting. A total of 725 patients were randomised to receive supportive care alone (n = 361) or supportive care plus cisplatin-based chemotherapy (n = 364). RESULTS: 65% of patients allocated chemotherapy (C) received all three cycles of treatment and a further 27% received one or two cycles. 74% of patients allocated no chemotherapy (NoC) received thoracic radiotherapy compared with 47% of the C group. Patients allocated C had a significantly better survival than those allocated NoC: HR 0.77 (95% CI 0.66 to 0.89, p = 0.0006), median survival 8.0 months for the C group v 5.7 months for the NoC group, a difference of 9 weeks. There were 19 (5%) treatment related deaths in the C group. There was no evidence that any subgroup benefited more or less fromchemotherapy. No significant differences were observed between the two groups in terms of the pre-defined primary and secondary quality of life end points, although large negative effects of chemotherapy were ruled out. The regimens used proved to be cost effective, the extra cost of chemotherapy being offset by longer survival. CONCLUSIONS: The survival benefit seen in this trial was entirely consistent with the NSCLC meta-analysis and subsequent similarly designed large trials. The information on quality of life and cost should enablepatients and their clinicians to make more informed treatment choices

Aboriginal life pathways through multiple human service domains; administrative data linkage for policy

Aboriginal children and families face the highest levels of disadvantage of any population group in Australia across health, education, child protection, justice and other human service domains, but longitudinal data to inform policy is scant. The Western Australian Aboriginal Child Health Survey (WAACHS) is a population representative cross-sectional child development study of over 5,000 randomly selected children aged 0-17 years, plus their families and schools, conducted between 2000 and 2002. This project seeks to leverage the WAACHS by linking the survey data for all participants with State administrative human services data registers from the previous 30+ years, to develop a major program of work in Aboriginal Human Development that would be unique in the world. This presentation describes the project history, novel survey linkage methodology, and project aims in the policy domain

#Bieber + #Blast = #BieberBlast: Early Prediction of Popular Hashtag Compounds

Compounding of natural language units is a very common phenomena. In this paper, we show, for the first time, that Twitter hashtags which, could be considered as correlates of such linguistic units, undergo compounding. We identify reasons for this compounding and propose a prediction model that can identify with 77.07% accuracy if a pair of hashtags compounding in the near future (i.e., 2 months after compounding) shall become popular. At longer times T = 6, 10 months the accuracies are 77.52% and 79.13% respectively. This technique has strong implications to trending hashtag recommendation since newly formed hashtag compounds can be recommended early, even before the compounding has taken place. Further, humans can predict compounds with an overall accuracy of only 48.7% (treated as baseline). Notably, while humans can discriminate the relatively easier cases, the automatic framework is successful in classifying the relatively harder cases.Comment: 14 pages, 4 figures, 9 tables, published in CSCW (Computer-Supported Cooperative Work and Social Computing) 2016. in Proceedings of 19th ACM conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2016

Utterance Selection Model of Language Change

We present a mathematical formulation of a theory of language change. The theory is evolutionary in nature and has close analogies with theories of population genetics. The mathematical structure we construct similarly has correspondences with the Fisher-Wright model of population genetics, but there are significant differences. The continuous time formulation of the model is expressed in terms of a Fokker-Planck equation. This equation is exactly soluble in the case of a single speaker and can be investigated analytically in the case of multiple speakers who communicate equally with all other speakers and give their utterances equal weight. Whilst the stationary properties of this system have much in common with the single-speaker case, time-dependent properties are richer. In the particular case where linguistic forms can become extinct, we find that the presence of many speakers causes a two-stage relaxation, the first being a common marginal distribution that persists for a long time as a consequence of ultimate extinction being due to rare fluctuations.Comment: 21 pages, 17 figure

Introduction

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/69081/2/10.1177_0261927X99018001001.pd

Does knowledge of cancer diagnosis affect quality of life? A methodological challenge

BACKGROUND: As part of an assessment of quality of life in lung cancer patients an investigation was carried out to examine whether the knowledge of their diagnosis affected their quality of life. METHODS: Every patient in a defined geographical area with a potential diagnosis of lung cancer was interviewed at first consultation and after a definitive treatment has been given. Quality of life was assessed using three standard measures: the Nottingham Health Profile (NHP), the EORTC quality of life questionnaire (QLQ-C30) and its lung cancer supplementary questionnaire (QLQ-LC13). Comparison was made in quality of life scores between patients who knew their cancer diagnosis and those who did not. RESULTS: In all, 129 lung cancer patients were interviewed. Of these, 30 patients (23%) knew and 99 (78%) did not know their cancer diagnosis at the time of baseline assessment. The patient groups were similar in their characteristics except for age (P = 0.04) and cell type (P < 0.0001). Overall, there were no significant differences between these two groups with regard to their scores on the three instruments used. A major finding was that both group scored almost the same on emotional reactions (P = 0.8) and social isolation (P = 1.0) as measured by the NHP, and emotional (P = 0.7) and social functioning (P = 1.0) as measured by the EORTC QLQ-C30. In addition there were no significant differences in patients' symptom scores between those who knew their diagnosis and those who did not, nor did any consistent pattern emerge. The only significant difference was for sleep difficulties (P = 0.02). CONCLUSION: The findings suggest that the knowledge of cancer diagnosis does not affect the way in which patients respond to quality of life questionnaires