The Experts below are selected from a list of 705768 Experts worldwide ranked by ideXlab platform
Brian R Lane - One of the best experts on this subject based on the ideXlab platform.
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critical appraisal of first Generation renal tumor complexity scoring systems creation of a second Generation Model of tumor complexity
Urologic Oncology-seminars and Original Investigations, 2015Co-Authors: Conrad M Tobert, Allen Shoemaker, Richard J Kahnoski, Brian R LaneAbstract:Abstract Objective To investigate whether a combination of variables from each nephrometry system improves performance. There are 3 first-Generation systems that quantify tumor complexity: R.E.N.A.L. nephrometry score (RNS), preoperative aspects and dimensions used for an anatomical (PADUA) classification (PC), and centrality index (CI). Although each has been subjected to validation and comparative analysis, to our knowledge, no work has been done to combine variables from each method to optimize their performance. Patients and methods Scores were assigned to each of 276 patients undergoing partial nephrectomy (PN) or radical nephrectomy (RN). Individual components of all 3 systems were evaluated in multivariable logistic regression analysis of surgery type (PN vs. RN) and combined into a “second-Generation Model.” Results In multivariable analysis, each scoring system was a significant predictor of PN vs. RN ( P P P Conclusions Optimization of first-Generation Models of renal tumor complexity results in a novel scoring system, which strongly predicts surgery type. This second-Generation Model should aid comprehension, but future work is still needed to establish the most clinically useful Model.
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critical appraisal of first Generation renal tumor complexity scoring systems creation of a second Generation Model of tumor complexity
Urologic Oncology-seminars and Original Investigations, 2015Co-Authors: Conrad M Tobert, Allen Shoemaker, Richard J Kahnoski, Brian R LaneAbstract:Abstract Objective To investigate whether a combination of variables from each nephrometry system improves performance. There are 3 first-Generation systems that quantify tumor complexity: R.E.N.A.L. nephrometry score (RNS), preoperative aspects and dimensions used for an anatomical (PADUA) classification (PC), and centrality index (CI). Although each has been subjected to validation and comparative analysis, to our knowledge, no work has been done to combine variables from each method to optimize their performance. Patients and methods Scores were assigned to each of 276 patients undergoing partial nephrectomy (PN) or radical nephrectomy (RN). Individual components of all 3 systems were evaluated in multivariable logistic regression analysis of surgery type (PN vs. RN) and combined into a “second-Generation Model.” Results In multivariable analysis, each scoring system was a significant predictor of PN vs. RN ( P P P Conclusions Optimization of first-Generation Models of renal tumor complexity results in a novel scoring system, which strongly predicts surgery type. This second-Generation Model should aid comprehension, but future work is still needed to establish the most clinically useful Model.
Conrad M Tobert - One of the best experts on this subject based on the ideXlab platform.
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critical appraisal of first Generation renal tumor complexity scoring systems creation of a second Generation Model of tumor complexity
Urologic Oncology-seminars and Original Investigations, 2015Co-Authors: Conrad M Tobert, Allen Shoemaker, Richard J Kahnoski, Brian R LaneAbstract:Abstract Objective To investigate whether a combination of variables from each nephrometry system improves performance. There are 3 first-Generation systems that quantify tumor complexity: R.E.N.A.L. nephrometry score (RNS), preoperative aspects and dimensions used for an anatomical (PADUA) classification (PC), and centrality index (CI). Although each has been subjected to validation and comparative analysis, to our knowledge, no work has been done to combine variables from each method to optimize their performance. Patients and methods Scores were assigned to each of 276 patients undergoing partial nephrectomy (PN) or radical nephrectomy (RN). Individual components of all 3 systems were evaluated in multivariable logistic regression analysis of surgery type (PN vs. RN) and combined into a “second-Generation Model.” Results In multivariable analysis, each scoring system was a significant predictor of PN vs. RN ( P P P Conclusions Optimization of first-Generation Models of renal tumor complexity results in a novel scoring system, which strongly predicts surgery type. This second-Generation Model should aid comprehension, but future work is still needed to establish the most clinically useful Model.
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critical appraisal of first Generation renal tumor complexity scoring systems creation of a second Generation Model of tumor complexity
Urologic Oncology-seminars and Original Investigations, 2015Co-Authors: Conrad M Tobert, Allen Shoemaker, Richard J Kahnoski, Brian R LaneAbstract:Abstract Objective To investigate whether a combination of variables from each nephrometry system improves performance. There are 3 first-Generation systems that quantify tumor complexity: R.E.N.A.L. nephrometry score (RNS), preoperative aspects and dimensions used for an anatomical (PADUA) classification (PC), and centrality index (CI). Although each has been subjected to validation and comparative analysis, to our knowledge, no work has been done to combine variables from each method to optimize their performance. Patients and methods Scores were assigned to each of 276 patients undergoing partial nephrectomy (PN) or radical nephrectomy (RN). Individual components of all 3 systems were evaluated in multivariable logistic regression analysis of surgery type (PN vs. RN) and combined into a “second-Generation Model.” Results In multivariable analysis, each scoring system was a significant predictor of PN vs. RN ( P P P Conclusions Optimization of first-Generation Models of renal tumor complexity results in a novel scoring system, which strongly predicts surgery type. This second-Generation Model should aid comprehension, but future work is still needed to establish the most clinically useful Model.
Richard J Kahnoski - One of the best experts on this subject based on the ideXlab platform.
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critical appraisal of first Generation renal tumor complexity scoring systems creation of a second Generation Model of tumor complexity
Urologic Oncology-seminars and Original Investigations, 2015Co-Authors: Conrad M Tobert, Allen Shoemaker, Richard J Kahnoski, Brian R LaneAbstract:Abstract Objective To investigate whether a combination of variables from each nephrometry system improves performance. There are 3 first-Generation systems that quantify tumor complexity: R.E.N.A.L. nephrometry score (RNS), preoperative aspects and dimensions used for an anatomical (PADUA) classification (PC), and centrality index (CI). Although each has been subjected to validation and comparative analysis, to our knowledge, no work has been done to combine variables from each method to optimize their performance. Patients and methods Scores were assigned to each of 276 patients undergoing partial nephrectomy (PN) or radical nephrectomy (RN). Individual components of all 3 systems were evaluated in multivariable logistic regression analysis of surgery type (PN vs. RN) and combined into a “second-Generation Model.” Results In multivariable analysis, each scoring system was a significant predictor of PN vs. RN ( P P P Conclusions Optimization of first-Generation Models of renal tumor complexity results in a novel scoring system, which strongly predicts surgery type. This second-Generation Model should aid comprehension, but future work is still needed to establish the most clinically useful Model.
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critical appraisal of first Generation renal tumor complexity scoring systems creation of a second Generation Model of tumor complexity
Urologic Oncology-seminars and Original Investigations, 2015Co-Authors: Conrad M Tobert, Allen Shoemaker, Richard J Kahnoski, Brian R LaneAbstract:Abstract Objective To investigate whether a combination of variables from each nephrometry system improves performance. There are 3 first-Generation systems that quantify tumor complexity: R.E.N.A.L. nephrometry score (RNS), preoperative aspects and dimensions used for an anatomical (PADUA) classification (PC), and centrality index (CI). Although each has been subjected to validation and comparative analysis, to our knowledge, no work has been done to combine variables from each method to optimize their performance. Patients and methods Scores were assigned to each of 276 patients undergoing partial nephrectomy (PN) or radical nephrectomy (RN). Individual components of all 3 systems were evaluated in multivariable logistic regression analysis of surgery type (PN vs. RN) and combined into a “second-Generation Model.” Results In multivariable analysis, each scoring system was a significant predictor of PN vs. RN ( P P P Conclusions Optimization of first-Generation Models of renal tumor complexity results in a novel scoring system, which strongly predicts surgery type. This second-Generation Model should aid comprehension, but future work is still needed to establish the most clinically useful Model.
Allen Shoemaker - One of the best experts on this subject based on the ideXlab platform.
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critical appraisal of first Generation renal tumor complexity scoring systems creation of a second Generation Model of tumor complexity
Urologic Oncology-seminars and Original Investigations, 2015Co-Authors: Conrad M Tobert, Allen Shoemaker, Richard J Kahnoski, Brian R LaneAbstract:Abstract Objective To investigate whether a combination of variables from each nephrometry system improves performance. There are 3 first-Generation systems that quantify tumor complexity: R.E.N.A.L. nephrometry score (RNS), preoperative aspects and dimensions used for an anatomical (PADUA) classification (PC), and centrality index (CI). Although each has been subjected to validation and comparative analysis, to our knowledge, no work has been done to combine variables from each method to optimize their performance. Patients and methods Scores were assigned to each of 276 patients undergoing partial nephrectomy (PN) or radical nephrectomy (RN). Individual components of all 3 systems were evaluated in multivariable logistic regression analysis of surgery type (PN vs. RN) and combined into a “second-Generation Model.” Results In multivariable analysis, each scoring system was a significant predictor of PN vs. RN ( P P P Conclusions Optimization of first-Generation Models of renal tumor complexity results in a novel scoring system, which strongly predicts surgery type. This second-Generation Model should aid comprehension, but future work is still needed to establish the most clinically useful Model.
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critical appraisal of first Generation renal tumor complexity scoring systems creation of a second Generation Model of tumor complexity
Urologic Oncology-seminars and Original Investigations, 2015Co-Authors: Conrad M Tobert, Allen Shoemaker, Richard J Kahnoski, Brian R LaneAbstract:Abstract Objective To investigate whether a combination of variables from each nephrometry system improves performance. There are 3 first-Generation systems that quantify tumor complexity: R.E.N.A.L. nephrometry score (RNS), preoperative aspects and dimensions used for an anatomical (PADUA) classification (PC), and centrality index (CI). Although each has been subjected to validation and comparative analysis, to our knowledge, no work has been done to combine variables from each method to optimize their performance. Patients and methods Scores were assigned to each of 276 patients undergoing partial nephrectomy (PN) or radical nephrectomy (RN). Individual components of all 3 systems were evaluated in multivariable logistic regression analysis of surgery type (PN vs. RN) and combined into a “second-Generation Model.” Results In multivariable analysis, each scoring system was a significant predictor of PN vs. RN ( P P P Conclusions Optimization of first-Generation Models of renal tumor complexity results in a novel scoring system, which strongly predicts surgery type. This second-Generation Model should aid comprehension, but future work is still needed to establish the most clinically useful Model.
Sandra R B Allerheiligen - One of the best experts on this subject based on the ideXlab platform.
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next Generation Model based drug discovery and development quantitative and systems pharmacology
Clinical Pharmacology & Therapeutics, 2010Co-Authors: Sandra R B AllerheiligenAbstract:Pharmaceutical researchers have undertaken many initiatives and technologies to stem the rising costs of drug discovery and development. Biomarkers, adaptive trial designs, Modeling, trial simulations, predictive metabolism, data mining, and disease Models have reshaped the way in which researchers approach discovery and development. Quantitative pharmacology (QP), which leverages Model-based approaches, operates at both cultural and technical levels to integrate data and scientific disciplines so as to utilize existing knowledge while concomitantly enhancing the ability to make predictions about future experiments and results. Clinical Pharmacology & Therapeutics (2010) 88 1, 135–137. doi: 10.1038/clpt.2010.81
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Next‐Generation Model‐Based Drug Discovery and Development: Quantitative and Systems Pharmacology
Clinical Pharmacology & Therapeutics, 2010Co-Authors: Sandra R B AllerheiligenAbstract:Pharmaceutical researchers have undertaken many initiatives and technologies to stem the rising costs of drug discovery and development. Biomarkers, adaptive trial designs, Modeling, trial simulations, predictive metabolism, data mining, and disease Models have reshaped the way in which researchers approach discovery and development. Quantitative pharmacology (QP), which leverages Model-based approaches, operates at both cultural and technical levels to integrate data and scientific disciplines so as to utilize existing knowledge while concomitantly enhancing the ability to make predictions about future experiments and results. Clinical Pharmacology & Therapeutics (2010) 88 1, 135–137. doi: 10.1038/clpt.2010.81
