The Experts below are selected from a list of 96 Experts worldwide ranked by ideXlab platform
Hugo D Critchley - One of the best experts on this subject based on the ideXlab platform.
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decision making under risk a graph based network analysis using functional mri
NeuroImage, 2012Co-Authors: Ludovico Minati, Marina Grisoli, Anil K Seth, Hugo D CritchleyAbstract:Adaptive behavior requires choosing effectively between options involving risks and potential rewards. Existing studies implicate lateral and medial prefrontal areas, striatum, insula, amygdala and parietal regions in specific aspects of decision-making. However, limited attention is given to how brain networks encode economic parameters in patterns of inter-regional interactions. Here, healthy participants underwent functional MRI while evaluating “mixed” gambles presenting potential gains, losses and associated Outcome probabilities. Connectivity graphs were constructed from analyses of psychophysiological interactions across a comprehensive atlas of brain regions. Expected value correlated positively with activity within medial prefrontal and occipital cortices, and modulated effective connectivity across a network that extended substantially beyond these nodes. Value-sensitive effective connections were found to be arranged as a unitary, small world network in which medial and anterior–lateral prefrontal areas featured as hubs, characterized by dense connectivity and high shortest-path centrality. Further analyses revealed that the observed effective connectivity effects were more pertinent to dichotomous gain/loss comparisons than to continuous value determination. Factoring expected value into its constituent components, potential loss modulated connectivity across a subset of the value-sensitive network, whereas potential gain and Outcome Probability were not significantly embodied in functional interactions. Regional response non-linearity was excluded as an artifactual basis to the observed effects, and directionality inferences were confirmed by comparison of dynamic causal models. Our findings extend current literature demonstrating that the representation of value is dependent on distributed processing taking across a widespread network which feeds information into a limited set of integrative prefrontal nodes. This study also has more general paradigmatic implications for neuroeconomics, demonstrating the value of explicit modeling of inter-regional interactions for understanding the neural substrates of decisional processes.
J W R Nortier - One of the best experts on this subject based on the ideXlab platform.
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the in or exclusion of non breast cancer related death and contralateral breast cancer significantly affects estimated Outcome Probability in early breast cancer
Breast Cancer Research and Treatment, 2008Co-Authors: R A Nout, W E Fiets, Henk Struikmans, F R Rosendaal, Hein Putter, J W R NortierAbstract:A wide variation of definitions of recurrent disease and survival are used in the analyses of Outcome of patients with early breast cancer. Explicit definitions with details both on endpoints and censoring are provided in less than half of published studies. We evaluated the effects of various definitions of survival and recurrent disease on estimated Outcome in a prospectively determined cohort of 463 patients with primary breast cancer. Outcome estimates were determined both by the Kaplan–Meier and a competing risk method. In- or exclusion of contralateral breast cancer or non-disease related death in the definition of recurrent disease or survival significantly affects estimated Outcome Probability. The magnitude of this finding was dependent on patient-, tumour-, and treatment characteristics. Knowledge of the contribution of non-disease related death or contralateral breast cancer to estimated recurrent disease rate and overall death rate is indispensable for a correct interpretation and comparison of Outcome analyses.
Geoff Donnan - One of the best experts on this subject based on the ideXlab platform.
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improved ischemic stroke Outcome prediction using model estimation of Outcome Probability the thrive c calculation
International Journal of Stroke, 2015Co-Authors: Alexander C Flint, Vivek A Rao, Sheila L Chan, Sean P Cullen, Bonnie Faigeles, Wade S Smith, Philip M W Bath, Nils Wahlgren, Niaz Ahmed, Geoff DonnanAbstract:Background and purpose The Totaled Health Risks in Vascular Events (THRIVE) score is a previously validated ischemic stroke Outcome prediction tool. Although simplified scoring systems like the THRIVE score facilitate ease-of-use, when computers or devices are available at the point of care, a more accurate and patient-specific estimation of Outcome Probability should be possible by computing the logistic equation with patient-specific continuous variables. Methods We used data from 12 207 subjects from the Virtual International Stroke Trials Archive and the Safe Implementation of Thrombolysis in Stroke – Monitoring Study to develop and validate the performance of a model-derived estimation of Outcome Probability, the THRIVE-c calculation. Models were built with logistic regression using the underlying predictors from the THRIVE score: age, National Institutes of Health Stroke Scale score, and the Chronic Disease Scale (presence of hypertension, diabetes mellitus, or atrial fibrillation). Receiver operator characteristics analysis was used to assess model performance and compare the THRIVE-c model to the traditional THRIVE score, using a two-tailed Chi-squared test. Results The THRIVE-c model performed similarly in the randomly chosen development cohort (n = 6194, area under the curve = 0·786, 95% confidence interval 0·774–0·798) and validation cohort (n = 6013, area under the curve = 0·784, 95% confidence interval 0·772–0·796) (P = 0·79). Similar performance was also seen in two separate external validation cohorts. The THRIVE-c model (area under the curve = 0·785, 95% confidence interval 0·777–0·793) had superior performance when compared with the traditional THRIVE score (area under the curve = 0·746, 95% confidence interval 0·737–0·755) (P < 0·001). Conclusion By computing the logistic equation with patient-specific continuous variables in the THRIVE-c calculation, Outcomes at the individual patient level are more accurately estimated. Given the widespread availability of computers and devices at the point of care, such calculations can be easily performed with a simple user interface.
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abstract 13 improved ischemic stroke Outcome prediction using model estimation of Outcome Probability the thrive c calculator
Stroke, 2015Co-Authors: Alexander C Flint, Vivek A Rao, Sean P Cullen, Bonnie Faigeles, Wade S Smith, Philip M W Bath, Nils Wahlgren, Niaz Ahmed, Sheila Chan, Geoff DonnanAbstract:Introduction: The THRIVE score is a validated ischemic stroke Outcome prediction tool. While simplified systems like the THRIVE score facilitate ease-of-use, when computers or devices are available at the point of care, a more accurate estimation of Outcome Probability is possible by computing the logistic equation with patient-specific continuous variables. Methods: We used data from 12,207 subjects from the Virtual International Stroke Trials Archive (VISTA) and the Safe Implementation of Thrombolysis in Stroke - Monitoring Study (SITS-MOST) study to develop and validate the performance of a model-derived estimation of Outcome Probability, the THRIVE-c calculation. Models were built with logistic regression using the underlying predictors from the THRIVE score: age, NIH Stroke Scale (NIHSS) score, and the Chronic Disease Scale (presence of hypertension, diabetes mellitus, or atrial fibrillation). Receiver-Operator Characteristics (ROC) analysis was used to assess model performance and compare the THRIVE-c model to the traditional THRIVE score. Results: The THRIVE-c model performed similarly in the randomly chosen development cohort (n=6194, AUC = 0.786, 95% CI 0.774-0.798) and validation cohort (n=6013, AUC = 0.784, 95% CI 0.772-0.796) (P=0.79). The THRIVE-c model (AUC = 0.785, 95% CI 0.777-0.793) had superior performance when compared to the traditional THRIVE score (AUC = 0.746, 95% CI 0.737-0.755), (P<0.0001). Conclusion: By computing the logistic equation with patient-specific continuous variables in the THRIVE-c calculation, patient Outcomes are more accurately estimated. Given the widespread availability of the computers and devices at the point of care, such calculations can be easily performed with a simple user interface. A web calculator for THRIVE-c is available at www.thrivescore.org. ![][1] [1]: /embed/graphic-1.gif
R A Nout - One of the best experts on this subject based on the ideXlab platform.
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the in or exclusion of non breast cancer related death and contralateral breast cancer significantly affects estimated Outcome Probability in early breast cancer
Breast Cancer Research and Treatment, 2008Co-Authors: R A Nout, W E Fiets, Henk Struikmans, F R Rosendaal, Hein Putter, J W R NortierAbstract:A wide variation of definitions of recurrent disease and survival are used in the analyses of Outcome of patients with early breast cancer. Explicit definitions with details both on endpoints and censoring are provided in less than half of published studies. We evaluated the effects of various definitions of survival and recurrent disease on estimated Outcome in a prospectively determined cohort of 463 patients with primary breast cancer. Outcome estimates were determined both by the Kaplan–Meier and a competing risk method. In- or exclusion of contralateral breast cancer or non-disease related death in the definition of recurrent disease or survival significantly affects estimated Outcome Probability. The magnitude of this finding was dependent on patient-, tumour-, and treatment characteristics. Knowledge of the contribution of non-disease related death or contralateral breast cancer to estimated recurrent disease rate and overall death rate is indispensable for a correct interpretation and comparison of Outcome analyses.
Alexander C Flint - One of the best experts on this subject based on the ideXlab platform.
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improved ischemic stroke Outcome prediction using model estimation of Outcome Probability the thrive c calculation
International Journal of Stroke, 2015Co-Authors: Alexander C Flint, Vivek A Rao, Sheila L Chan, Sean P Cullen, Bonnie Faigeles, Wade S Smith, Philip M W Bath, Nils Wahlgren, Niaz Ahmed, Geoff DonnanAbstract:Background and purpose The Totaled Health Risks in Vascular Events (THRIVE) score is a previously validated ischemic stroke Outcome prediction tool. Although simplified scoring systems like the THRIVE score facilitate ease-of-use, when computers or devices are available at the point of care, a more accurate and patient-specific estimation of Outcome Probability should be possible by computing the logistic equation with patient-specific continuous variables. Methods We used data from 12 207 subjects from the Virtual International Stroke Trials Archive and the Safe Implementation of Thrombolysis in Stroke – Monitoring Study to develop and validate the performance of a model-derived estimation of Outcome Probability, the THRIVE-c calculation. Models were built with logistic regression using the underlying predictors from the THRIVE score: age, National Institutes of Health Stroke Scale score, and the Chronic Disease Scale (presence of hypertension, diabetes mellitus, or atrial fibrillation). Receiver operator characteristics analysis was used to assess model performance and compare the THRIVE-c model to the traditional THRIVE score, using a two-tailed Chi-squared test. Results The THRIVE-c model performed similarly in the randomly chosen development cohort (n = 6194, area under the curve = 0·786, 95% confidence interval 0·774–0·798) and validation cohort (n = 6013, area under the curve = 0·784, 95% confidence interval 0·772–0·796) (P = 0·79). Similar performance was also seen in two separate external validation cohorts. The THRIVE-c model (area under the curve = 0·785, 95% confidence interval 0·777–0·793) had superior performance when compared with the traditional THRIVE score (area under the curve = 0·746, 95% confidence interval 0·737–0·755) (P < 0·001). Conclusion By computing the logistic equation with patient-specific continuous variables in the THRIVE-c calculation, Outcomes at the individual patient level are more accurately estimated. Given the widespread availability of computers and devices at the point of care, such calculations can be easily performed with a simple user interface.
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abstract 13 improved ischemic stroke Outcome prediction using model estimation of Outcome Probability the thrive c calculator
Stroke, 2015Co-Authors: Alexander C Flint, Vivek A Rao, Sean P Cullen, Bonnie Faigeles, Wade S Smith, Philip M W Bath, Nils Wahlgren, Niaz Ahmed, Sheila Chan, Geoff DonnanAbstract:Introduction: The THRIVE score is a validated ischemic stroke Outcome prediction tool. While simplified systems like the THRIVE score facilitate ease-of-use, when computers or devices are available at the point of care, a more accurate estimation of Outcome Probability is possible by computing the logistic equation with patient-specific continuous variables. Methods: We used data from 12,207 subjects from the Virtual International Stroke Trials Archive (VISTA) and the Safe Implementation of Thrombolysis in Stroke - Monitoring Study (SITS-MOST) study to develop and validate the performance of a model-derived estimation of Outcome Probability, the THRIVE-c calculation. Models were built with logistic regression using the underlying predictors from the THRIVE score: age, NIH Stroke Scale (NIHSS) score, and the Chronic Disease Scale (presence of hypertension, diabetes mellitus, or atrial fibrillation). Receiver-Operator Characteristics (ROC) analysis was used to assess model performance and compare the THRIVE-c model to the traditional THRIVE score. Results: The THRIVE-c model performed similarly in the randomly chosen development cohort (n=6194, AUC = 0.786, 95% CI 0.774-0.798) and validation cohort (n=6013, AUC = 0.784, 95% CI 0.772-0.796) (P=0.79). The THRIVE-c model (AUC = 0.785, 95% CI 0.777-0.793) had superior performance when compared to the traditional THRIVE score (AUC = 0.746, 95% CI 0.737-0.755), (P<0.0001). Conclusion: By computing the logistic equation with patient-specific continuous variables in the THRIVE-c calculation, patient Outcomes are more accurately estimated. Given the widespread availability of the computers and devices at the point of care, such calculations can be easily performed with a simple user interface. A web calculator for THRIVE-c is available at www.thrivescore.org. ![][1] [1]: /embed/graphic-1.gif
