Applications of the ACGT Master Ontology on Cancer

In this paper we present applications of the ACGT Master Ontology (MO) which is a new terminology resource for a transnational network providing data exchange in oncology, emphasizing the integration of both clinical and molecular data. The development of a new ontology was necessary due to problems with existing biomedical ontologies in oncology. The ACGT MO is a test case for the application of best practices in ontology development. This paper provides an overview of the application of the ontology within the ACGT project thus far

Heparanase is a prognostic indicator for postoperative survival in pancreatic carcinoma

British Journal of Cancer (2002) 87, 689–689. doi:10.1038/sj.bjc.6600504 www.bjcancer.co

Sowing the seeds of doubt: a narrative review on metacognitive training in schizophrenia

AbstractThe present article provides a narrative review of empirical studies on metacognitive training in psychosis (MCT). MCT represents an amalgam of cognitive-behavioral therapy (CBT), cognitive remediation (CRT) and psychoeducation. The intervention is available in either a group (MCT) or an individualized (MCT+) format. By sowing the seeds of doubt in a playful and entertaining fashion, the program targets positive symptoms, particularly delusions. It aims to raise patients’ awareness for common cognitive traps or biases (e.g., jumping to conclusions, overconfidence in errors, bias against disconfirmatory evidence) that are implicated in the formation and maintenance of psychosis. The majority of studies confirm that MCT meets its core aim, the reduction of delusions. Problems (e.g., potential allegiance effects) and knowledge gaps (i.e., outcome predictors) are highlighted. The preliminary data suggest that the individual MCT format is especially effective in addressing symptoms, cognitive biases and insight. We conclude that MCT appears to be a worthwhile complement to pharmacotherapy

Inspiratory muscle warm-up does not improve cycling time-trial performance

Purpose: This study examined the effects of an active cycling warm-up, with and without the addition of an inspiratory muscle warm-up (IMW), on 10-km cycling time-trial performance

Imputation of missing values of tumour stage in population-based cancer registration

<p>Abstract</p> <p>Background</p> <p>Missing data on tumour stage information is a common problem in population-based cancer registries. Statistical analyses on the level of tumour stage may be biased, if no adequate method for handling of missing data is applied. In order to determine a useful way to treat missing data on tumour stage, we examined different imputation models for multiple imputation with chained equations for analysing the stage-specific numbers of cases of malignant melanoma and female breast cancer.</p> <p>Methods</p> <p>This analysis was based on the malignant melanoma data set and the female breast cancer data set of the cancer registry Schleswig-Holstein, Germany. The cases with complete tumour stage information were extracted and their stage information partly removed according to a MAR missingness-pattern, resulting in five simulated data sets for each cancer entity. The missing tumour stage values were then treated with multiple imputation with chained equations, using polytomous regression, predictive mean matching, random forests and proportional sampling as imputation models. The estimated tumour stages, stage-specific numbers of cases and survival curves after multiple imputation were compared to the observed ones.</p> <p>Results</p> <p>The amount of missing values for malignant melanoma was too high to estimate a reasonable number of cases for each UICC stage. However, multiple imputation of missing stage values led to stage-specific numbers of cases of T-stage for malignant melanoma as well as T- and UICC-stage for breast cancer close to the observed numbers of cases. The observed tumour stages on the individual level, the stage-specific numbers of cases and the observed survival curves were best met with polytomous regression or predictive mean matching but not with random forest or proportional sampling as imputation models.</p> <p>Conclusions</p> <p>This limited simulation study indicates that multiple imputation with chained equations is an appropriate technique for dealing with missing information on tumour stage in population-based cancer registries, if the amount of unstaged cases is on a reasonable level.</p