Pretreatment Tables Predicting Pathologic Stage of Locally Advanced Prostate Cancer

BACKGROUND Pretreatment tables for the prediction of pathologic stage have been published and validated for localized prostate cancer (PCa). No such tables are available for locally advanced (cT3a) PCa. OBJECTIVE To construct tables predicting pathologic outcome after radical prostatectomy (RP) for patients with cT3a PCa with the aim to help guide treatment decisions in clinical practice. DESIGN, SETTING, AND PARTICIPANTS This was a multicenter retrospective cohort study including 759 consecutive patients with cT3a PCa treated with RP between 1987 and 2010. INTERVENTION Retropubic RP and pelvic lymphadenectomy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Patients were divided into pretreatment prostate-specific antigen (PSA) and biopsy Gleason score (GS) subgroups. These parameters were used to construct tables predicting pathologic outcome and the presence of positive lymph nodes (LNs) after RP for cT3a PCa using ordinal logistic regression. RESULTS AND LIMITATIONS In the model predicting pathologic outcome, the main effects of biopsy GS and pretreatment PSA were significant. A higher GS and/or higher PSA level was associated with a more unfavorable pathologic outcome. The validation procedure, using a repeated split-sample method, showed good predictive ability. Regression analysis also showed an increasing probability of positive LNs with increasing PSA levels and/or higher GS. Limitations of the study are the retrospective design and the long study period. CONCLUSIONS These novel tables predict pathologic stage after RP for patients with cT3a PCa based on pretreatment PSA level and biopsy GS. They can be used to guide decision making in men with locally advanced PCa. PATIENT SUMMARY Our study might provide physicians with a useful tool to predict pathologic stage in locally advanced prostate cancer that might help select patients who may need multimodal treatment.

The social background of political decision-makers.

"Sources for tables": p. 63-64. Bibliographical references included in "Footnotes to the study"(p.65-71)

The differential effects of *American and British trial proceedings on juror decision-making

Seven basic elements differentiate British from American trial procedures: confining attorneys to their tables; dealing with objections outside the presence of the jury; resolving disagreements between attorneys prior to objections being made; presenting the defense opening statement at the close of the prosecution case; the judge directly questions witnesses and has a wider latitude in controlling the evidence; and the judge gives a summation of all the evidence presented to the jury (Fulero & Turner, 1997). The present experiment examined the influence of these different courtroom procedures, judges' non-verbal behavior, and evidence strength on juror decision-making. Using models of persuasion to understand how the varying elements may effect juror decision-making, it was predicted that trials following American courtroom procedures would be more distracting for jurors and as such, they would be more likely to rely on the peripheral cue of the judge's expectations for trial outcome as expressed in his nonverbal behavior. In trials following British procedures jurors should be less distracted and better able to scrutinize the strength of the evidence that in turn should minimize the influence of the judge's nonverbal behavior. Two hundred forty-five participants viewed a mock civil trial in which courtroom procedure, judge's nonverbal behavior, and evidence strength were varied. Analyses suggest that courtroom procedure and evidence strength influenced the direction of participants' verdicts, but that judge's nonverbal behavior did not have a direct impact on verdict preference. Judge's nonverbal behavior appeared to influence other measures related to verdict. Participants were more confident in their verdicts when they agreed with judge's nonverbal behavior and when they viewed British courtroom procedures. Participants were more likely to return estimates of the defendant's liability that reflected judge's nonverbal behavior and a congruency with evidence strength. Participants also recalled more facts in the British conditions than in the American conditions. These findings are interpreted as indicating the importance of the impact of trial procedures and of nonverbal influence. ^

Data to Decision in a Dynamic Ocean: Robust Species Distribution Models and Spatial Decision Frameworks

Human use of the oceans is increasingly in conflict with conservation of endangered species. Methods for managing the spatial and temporal placement of industries such as military, fishing, transportation and offshore energy, have historically been post hoc; i.e. the time and place of human activity is often already determined before assessment of environmental impacts. In this dissertation, I build robust species distribution models in two case study areas, US Atlantic (Best et al. 2012) and British Columbia (Best et al. 2015), predicting presence and abundance respectively, from scientific surveys. These models are then applied to novel decision frameworks for preemptively suggesting optimal placement of human activities in space and time to minimize ecological impacts: siting for offshore wind energy development, and routing ships to minimize risk of striking whales. Both decision frameworks relate the tradeoff between conservation risk and industry profit with synchronized variable and map views as online spatial decision support systems.

For siting offshore wind energy development (OWED) in the U.S. Atlantic (chapter 4), bird density maps are combined across species with weights of OWED sensitivity to collision and displacement and 10 km2 sites are compared against OWED profitability based on average annual wind speed at 90m hub heights and distance to transmission grid. A spatial decision support system enables toggling between the map and tradeoff plot views by site. A selected site can be inspected for sensitivity to a cetaceans throughout the year, so as to capture months of the year which minimize episodic impacts of pre-operational activities such as seismic airgun surveying and pile driving.

Routing ships to avoid whale strikes (chapter 5) can be similarly viewed as a tradeoff, but is a different problem spatially. A cumulative cost surface is generated from density surface maps and conservation status of cetaceans, before applying as a resistance surface to calculate least-cost routes between start and end locations, i.e. ports and entrance locations to study areas. Varying a multiplier to the cost surface enables calculation of multiple routes with different costs to conservation of cetaceans versus cost to transportation industry, measured as distance. Similar to the siting chapter, a spatial decisions support system enables toggling between the map and tradeoff plot view of proposed routes. The user can also input arbitrary start and end locations to calculate the tradeoff on the fly.

Essential to the input of these decision frameworks are distributions of the species. The two preceding chapters comprise species distribution models from two case study areas, U.S. Atlantic (chapter 2) and British Columbia (chapter 3), predicting presence and density, respectively. Although density is preferred to estimate potential biological removal, per Marine Mammal Protection Act requirements in the U.S., all the necessary parameters, especially distance and angle of observation, are less readily available across publicly mined datasets.

In the case of predicting cetacean presence in the U.S. Atlantic (chapter 2), I extracted datasets from the online OBIS-SEAMAP geo-database, and integrated scientific surveys conducted by ship (n=36) and aircraft (n=16), weighting a Generalized Additive Model by minutes surveyed within space-time grid cells to harmonize effort between the two survey platforms. For each of 16 cetacean species guilds, I predicted the probability of occurrence from static environmental variables (water depth, distance to shore, distance to continental shelf break) and time-varying conditions (monthly sea-surface temperature). To generate maps of presence vs. absence, Receiver Operator Characteristic (ROC) curves were used to define the optimal threshold that minimizes false positive and false negative error rates. I integrated model outputs, including tables (species in guilds, input surveys) and plots (fit of environmental variables, ROC curve), into an online spatial decision support system, allowing for easy navigation of models by taxon, region, season, and data provider.

For predicting cetacean density within the inner waters of British Columbia (chapter 3), I calculated density from systematic, line-transect marine mammal surveys over multiple years and seasons (summer 2004, 2005, 2008, and spring/autumn 2007) conducted by Raincoast Conservation Foundation. Abundance estimates were calculated using two different methods: Conventional Distance Sampling (CDS) and Density Surface Modelling (DSM). CDS generates a single density estimate for each stratum, whereas DSM explicitly models spatial variation and offers potential for greater precision by incorporating environmental predictors. Although DSM yields a more relevant product for the purposes of marine spatial planning, CDS has proven to be useful in cases where there are fewer observations available for seasonal and inter-annual comparison, particularly for the scarcely observed elephant seal. Abundance estimates are provided on a stratum-specific basis. Steller sea lions and harbour seals are further differentiated by ‘hauled out’ and ‘in water’. This analysis updates previous estimates (Williams & Thomas 2007) by including additional years of effort, providing greater spatial precision with the DSM method over CDS, novel reporting for spring and autumn seasons (rather than summer alone), and providing new abundance estimates for Steller sea lion and northern elephant seal. In addition to providing a baseline of marine mammal abundance and distribution, against which future changes can be compared, this information offers the opportunity to assess the risks posed to marine mammals by existing and emerging threats, such as fisheries bycatch, ship strikes, and increased oil spill and ocean noise issues associated with increases of container ship and oil tanker traffic in British Columbia’s continental shelf waters.

Starting with marine animal observations at specific coordinates and times, I combine these data with environmental data, often satellite derived, to produce seascape predictions generalizable in space and time. These habitat-based models enable prediction of encounter rates and, in the case of density surface models, abundance that can then be applied to management scenarios. Specific human activities, OWED and shipping, are then compared within a tradeoff decision support framework, enabling interchangeable map and tradeoff plot views. These products make complex processes transparent for gaming conservation, industry and stakeholders towards optimal marine spatial management, fundamental to the tenets of marine spatial planning, ecosystem-based management and dynamic ocean management.

Decision theory applied to image quality control in radiology

Background: The present work aims at the application of the decision theory to radiological image quality control ( QC) in diagnostic routine. The main problem addressed in the framework of decision theory is to accept or reject a film lot of a radiology service. The probability of each decision of a determined set of variables was obtained from the selected films. Methods: Based on a radiology service routine a decision probability function was determined for each considered group of combination characteristics. These characteristics were related to the film quality control. These parameters were also framed in a set of 8 possibilities, resulting in 256 possible decision rules. In order to determine a general utility application function to access the decision risk, we have used a simple unique parameter called r. The payoffs chosen were: diagnostic's result (correct/incorrect), cost (high/low), and patient satisfaction (yes/no) resulting in eight possible combinations. Results: Depending on the value of r, more or less risk will occur related to the decision-making. The utility function was evaluated in order to determine the probability of a decision. The decision was made with patients or administrators' opinions from a radiology service center. Conclusion: The model is a formal quantitative approach to make a decision related to the medical imaging quality, providing an instrument to discriminate what is really necessary to accept or reject a film or a film lot. The method presented herein can help to access the risk level of an incorrect radiological diagnosis decision.

DECISION TREE CLASSIFIERS FOR STAR/GALAXY SEPARATION

We study the star/galaxy classification efficiency of 13 different decision tree algorithms applied to photometric objects in the Sloan Digital Sky Survey Data Release Seven (SDSS-DR7). Each algorithm is defined by a set of parameters which, when varied, produce different final classification trees. We extensively explore the parameter space of each algorithm, using the set of 884,126 SDSS objects with spectroscopic data as the training set. The efficiency of star-galaxy separation is measured using the completeness function. We find that the Functional Tree algorithm (FT) yields the best results as measured by the mean completeness in two magnitude intervals: 14 <= r <= 21 (85.2%) and r >= 19 (82.1%). We compare the performance of the tree generated with the optimal FT configuration to the classifications provided by the SDSS parametric classifier, 2DPHOT, and Ball et al. We find that our FT classifier is comparable to or better in completeness over the full magnitude range 15 <= r <= 21, with much lower contamination than all but the Ball et al. classifier. At the faintest magnitudes (r > 19), our classifier is the only one that maintains high completeness (> 80%) while simultaneously achieving low contamination (similar to 2.5%). We also examine the SDSS parametric classifier (psfMag - modelMag) to see if the dividing line between stars and galaxies can be adjusted to improve the classifier. We find that currently stars in close pairs are often misclassified as galaxies, and suggest a new cut to improve the classifier. Finally, we apply our FT classifier to separate stars from galaxies in the full set of 69,545,326 SDSS photometric objects in the magnitude range 14 <= r <= 21.

Information from managing information systems as a fundamental element to support decision-making in hospitals

This article deals with the activity of defining information of hospital systems as fundamental for choosing the type of information systems to be used and also the organizational level to be supported. The use of hospital managing information systems improves the user`s decision -making process by allowing control report generation and following up the procedures made in the hospital as well.

Maternal decision on obtaining prenatal care: a study in Brazil

Objective: to address the social aspects of pregnancy and the views of pregnant women regarding prenatal assistance in Brazil. Design: this qualitative study was focused on describing the Social Representations of prenatal care held by pregnant women. The discourse of the collective subject (DCS) framework was used to analyse the data collected, within the theoretical background of social representations, as proposed and developed by Serge Moscovici. Participants and setting: 21 pregnant women who were users of the publicly funded Brazilian unified health-care system and resided in the area served by its family health programme in a low- to middle-income neighbourhood on the outskirts of Campo Grande, the capital of the state of Mato Grosso do Sul, in southwestern Brazil. Data were collected by conducting in-depth, face-to-face interviews from January to October 2006. Findings: all participants were married. Formal education of the participants was less than five years in four cases, between five and eight years in six cases, and greater than 11 years in 10 cases. Nine participants had informal jobs and earned up to US$ 200 per month, four paricipants had administrative jobs and earned over US$ 500 per month, and eight participants did not work. No specific racial/ethnic background predominated. Lack of adherence to prenatal care allowed for the identification of two DCS themes: `organisation of prenatal care services` and `lifestyle features`. Key conclusions: the respondents were found to have negative feelings about pregnancy which manifest as many fears, including the fear of harming their children`s health, of being punished during labour, and of being reprimanded by health-care professionals for overlooking their prenatal care, in addition to the insecurity felt towards the infant and self. Implications for practice: the findings reveal that communication between pregnant women and healthcare professionals has been ineffective and that prenatal care has not been effective for the group interviewed-features that are likely to be found among other low- to middle-income groups living elsewhere in Brazil. (C) 2009 Elsevier Ltd. All rights reserved.

Financial distress, financial constraint and investment decision: Evidence from Brazil

This paper analyses the presence of financial constraint in the investment decisions of 367 Brazilian firms from 1997 to 2004, using a Bayesian econometric model with group-varying parameters. The motivation for this paper is the use of clustering techniques to group firms in a totally endogenous form. In order to classify the firms we used a hybrid clustering method, that is, hierarchical and non-hierarchical clustering techniques jointly. To estimate the parameters a Bayesian approach was considered. Prior distributions were assumed for the parameters, classifying the model in random or fixed effects. Ordinate predictive density criterion was used to select the model providing a better prediction. We tested thirty models and the better prediction considers the presence of 2 groups in the sample, assuming the fixed effect model with a Student t distribution with 20 degrees of freedom for the error. The results indicate robustness in the identification of financial constraint when the firms are classified by the clustering techniques. (C) 2010 Elsevier B.V. All rights reserved.

Decision making in fuzzy environment and multicriteria power engineering problems

This paper presents results of research into the use of the Bellman-Zadeh approach to decision making in a fuzzy environment for solving multicriteria power engineering problems. The application of the approach conforms to the principle of guaranteed result and provides constructive lines in computationally effective obtaining harmonious solutions on the basis of solving associated maxmin problems. The presented results are universally applicable and are already being used to solve diverse classes of power engineering problems. It is illustrated by considering problems of power and energy shortage allocation, power system operation, optimization of network configuration in distribution systems, and energetically effective voltage control in distribution systems. (c) 2011 Elsevier Ltd. All rights reserved.

A flexible consensus scheme for multicriteria group decision making under linguistic assessments

The present paper proposes a flexible consensus scheme for group decision making, which allows one to obtain a consistent collective opinion, from information provided by each expert in terms of multigranular fuzzy estimates. It is based on a linguistic hierarchical model with multigranular sets of linguistic terms, and the choice of the most suitable set is a prerogative of each expert. From the human viewpoint, using such model is advantageous, since it permits each expert to utilize linguistic terms that reflect more adequately the level of uncertainty intrinsic to his evaluation. From the operational viewpoint, the advantage of using such model lies in the fact that it allows one to express the linguistic information in a unique domain, without losses of information, during the discussion process. The proposed consensus scheme supposes that the moderator can interfere in the discussion process in different ways. The intervention can be a request to any expert to update his opinion or can be the adjustment of the weight of each expert`s opinion. An optimal adjustment can be achieved through the execution of an optimization procedure that searches for the weights that maximize a corresponding soft consensus index. In order to demonstrate the usefulness of the presented consensus scheme, a technique for multicriteria analysis, based on fuzzy preference relation modeling, is utilized for solving a hypothetical enterprise strategy planning problem, generated with the use of the Balanced Scorecard methodology. (C) 2009 Elsevier Inc. All rights reserved.

Multicriteria analysis in decision making under information uncertainty

This paper presents results of research related to multicriteria decision making under information uncertainty. The Bell-man-Zadeh approach to decision making in a fuzzy environment is utilized for analyzing multicriteria optimization models (< X, M > models) under deterministic information. Its application conforms to the principle of guaranteed result and provides constructive lines in obtaining harmonious solutions on the basis of analyzing associated maxmin problems. This circumstance permits one to generalize the classic approach to considering the uncertainty of quantitative information (based on constructing and analyzing payoff matrices reflecting effects which can be obtained for different combinations of solution alternatives and the so-called states of nature) in monocriteria decision making to multicriteria problems. Considering that the uncertainty of information can produce considerable decision uncertainty regions, the resolving capacity of this generalization does not always permit one to obtain unique solutions. Taking this into account, a proposed general scheme of multicriteria decision making under information uncertainty also includes the construction and analysis of the so-called < X, R > models (which contain fuzzy preference relations as criteria of optimality) as a means for the subsequent contraction of the decision uncertainty regions. The paper results are of a universal character and are illustrated by a simple example. (c) 2007 Elsevier Inc. All rights reserved.

Planning and operation of large-scale water distribution systems with preemptive priorities

This paper describes the development of an optimization model for the management and operation of a large-scale, multireservoir water supply distribution system with preemptive priorities. The model considers multiobjectives and hedging rules. During periods of drought, when water supply is insufficient to meet the planned demand, appropriate rationing factors are applied to reduce water supply. In this paper, a water distribution system is formulated as a network and solved by the GAMS modeling system for mathematical programming and optimization. A user-friendly interface is developed to facilitate the manipulation of data and to generate graphs and tables for decision makers. The optimization model and its interface form a decision support system (DSS), which can be used to configure a water distribution system to facilitate capacity expansion and reliability studies. Several examples are presented to demonstrate the utility and versatility of the developed DSS under different supply and demand scenarios, including applications to one of the largest water supply systems in the world, the Sao Paulo Metropolitan Area Water Supply Distribution System in Brazil.

Risk-based decision making method for maintenance policy selection of thermal power plant equipment

This study presents a decision-making method for maintenance policy selection of power plants equipment. The method is based on risk analysis concepts. The method first step consists in identifying critical equipment both for power plant operational performance and availability based on risk concepts. The second step involves the proposal of a potential maintenance policy that could be applied to critical equipment in order to increase its availability. The costs associated with each potential maintenance policy must be estimated, including the maintenance costs and the cost of failure that measures the critical equipment failure consequences for the power plant operation. Once the failure probabilities and the costs of failures are estimated, a decision-making procedure is applied to select the best maintenance policy. The decision criterion is to minimize the equipment cost of failure, considering the costs and likelihood of occurrence of failure scenarios. The method is applied to the analysis of a lubrication oil system used in gas turbines journal bearings. The turbine has more than 150 MW nominal output, installed in an open cycle thermoelectric power plant. A design modification with the installation of a redundant oil pump is proposed for lubricating oil system availability improvement. (C) 2009 Elsevier Ltd. All rights reserved.