Social media use and impact during the holiday travel planning process

Through an empirical study among holiday travellers, residing in the Former Soviet Union Republics, this paper presents a comprehensive view of role and impact of social media on the whole holiday travel planning process: Before, during and after the trip, providing insights on usage levels, scope of use, level of influence and trust. Findings suggest that social media are predominantly used after holidays for experience sharing. It is also shown that there is a strong correlation between perceived level of influence from social media and changes made in holiday plans prior to final decisions. Moreover, it is revealed that user-generated content is perceived as more trustworthy when compared to official tourism websites, travel agents and mass media advertising

Prognostic factors affecting survival after surgical resection of gastrointestinal stromal tumours: a two-unit experience over 10 years

BACKGROUND: Gastrointestinal stromal tumours (GISTs) are the most common mesenchymal neoplasm of the gastrointestinal (GI) tract which has only been recently described based on their specific immunohistochemistry and the presence of particular KIT-related mutations which potentially make them targets for tyrosine kinase inhibition. METHODS: Sixty-one patients (29 M; 32 F, median age 60 years; range: 23–86 years) between June 1994 and March 2005, were analyzed from two allied institutions. Patient, tumour, and treatment variables were analyzed to identify factors affecting survival. RESULTS: Of the 61 patients, 55 (90%) underwent complete surgical resection of macroscopic disease. The 5-year overall survival (OS) rate in the 61 patients was 88% and the 5-year disease-free survival (DFS) in the 55 cases completely resected was 75%. Univariate analysis revealed that R0 resection was strongly associated with a better OSrate (p < 0.0001). Likewise, univariate analysis also showed high mitotic count of > 10 mitoses/per 50 HPF was a significant variable in worse prognosis for OS (≤ 10 mitoses/per 50 HPF 95% 5-year OS vs. > 10 mitoses/per 50 HPF 74% 5-year OS, respectively; p = 0.013). On subsequent multivariate analysis, only high mitotic count remained as a significant negative prognostic variable for OS (p = 0.029). Among patients resected for cure, there were 8 recurrences during follow-up. The mean time to recurrence was 21 ± 10 months (range: 4–36 months). Univariate analysis revealed that mitotic count of > 10 mitoses per 50 high power fields, intratumoural necrosis, and pathological tumour size (> 10 cm in maximal diameter) significantly correlated with DFS (p = 0.006, 0.002 and 0.02, respectively), with tumour necrosis and high mitotic count remaining as independent predictive variables affecting prognosis on subsequent multivariate analysis. CONCLUSION: Most GISTs are resectable with survival principally dependent upon mitotic count and completeness of resection. Future metabolic and genetic analyses will define the role of and resistance to induction or postoperative adjuvant targeted kinase inhibition therapy

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Auditory event-related potentials

Auditory event related potentials are electric potentials (AERP, AEP) and magnetic fields (AEF) generated by the synchronous activity of large neural populations in the brain, which are time-locked to some actual or expected sound event

A Bayesian Approach to Graph Regression with Relevant Subgraph Selection

Many real-world applications with graph data require the efficient solution of a given regression task as well as the identification of the subgraphs which are relevant for the task. In these cases graphs are commonly represented as binary vectors of indicators of subgraphs, giving rise to an intractable input dimensionality. An efficient solution to this problem was recently proposed by a Lasso-type method where the objective function optimization over an intractable number of variables is reformulated as a dual mathematical programming problem over a small number of variables but a large number of constraints. The dual problem is then solved by column generation where the subgraphs corresponding to the most violated constraints are found by weighted subgraph mining. This paper proposes an extension of this method to a fully Bayesian approach which defines a prior distribution on the parameters and integrate them out from the model, thus providing a posterior distribution on the target variable as opposed to a single estimate. The advantage of this approach is that the extra information given by the target posterior distribution can be used for improving the model in several ways. In this paper, we use the target posterior variance as a measure of uncertainty in the prediction and show that, by rejecting unconfident predictions, we can improve state-of-the-art performance on several molecular graph datasets