Basis Of Decision Rules Research Articles

Verification of relationships between anthropometric variables among ureteral stents recipients and ureteric lengths: a challenge for Vitruvian-da Vinci theory.

ObjectiveTo determine and verify how anthropometric variables correlate to ureteric lengths and how well statistical models approximate the actual ureteric lengths.Materials and methodsIn this work, 129 charts of endourological patients (71 females and 58 males) were studied retrospectively. Data were gathered from various research centers from North and South America. Continuous data were studied using descriptive statistics. Anthropometric variables (age, body surface area, body weight, obesity, and stature) were utilized as predictors of ureteric lengths. Linear regressions and correlations were used for studying relationships between the predictors and the outcome variables (ureteric lengths); P-value was set at 0.05. To assess how well statistical models were capable of predicting the actual ureteric lengths, percentages (or ratios of matched to mismatched results) were employed.ResultsThe results of the study show that anthropometric variables do not correlate well to ureteric lengths. Statistical models can partially estimate ureteric lengths. Out of the five anthropometric variables studied, three of them: body frame, stature, and weight, each with a P<0.0001, were significant. Two of the variables: age (R2=0.01; P=0.20) and obesity (R2=0.03; P=0.06), were found to be poor estimators of ureteric lengths. None of the predictors reached the expected (match:above:below) ratio of 1:0:0 to qualify as reliable predictors of ureteric lengths.ConclusionThere is not sufficient evidence to conclude that anthropometric variables can reliably predict ureteric lengths. These variables appear to lack adequate specificity as they failed to reach the expected (match:above:below) ratio of 1:0:0. Consequently, selections of ureteral stents continue to remain a challenge. However, height (R2=0.68) with the (match:above:below) ratio of 3:3:4 appears suited for use as estimator, but on the basis of decision rule. Additional research is recommended for stent improvements and ureteric length determinations.

Conservation policies and planning under climate change

Biodiversity conservation policies focus on securing the survival of species and habitats according to their current distribution. This basic premise may be inappropriate for halting biodiversity decline under the dynamic changes caused by climate change. This study explores a dynamic spatial conservation prioritization problem where climate change gradually changes the future habitat suitability of a site’ current species. This has implications for survival probability, as well as for species that potentially immigrate to the site. The problem is explored using a set of heuristics for both of two policy objectives focusing on (1) the protection on current (native) species, and (2) all species, including immigrating species. The trade-offs between the protection of native species versus all species is illustrated. The study shows that the development of prediction models of future species distributions as the basis of decision rules can be crucial for ensuring the effectiveness of conservation plans. Finally, it is discussed how more adaptive strategies, that allow for the redirection of resources from protected sites to privately-owned sites, may increase the effectiveness of the conservation networks. Climate change induced shifts in the suitability of habitats for species may increase the value of such adaptive strategies, the benefit decreasing with increasing migration probabilities and species distribution dynamics.

The role of medical necessity and cost-effectiveness in making medical decisions.

The term "medical necessity" is used ubiquitously in health care, but its meaning and implementation vary substantially among providers, payers, and patients. This ambiguity has led some to suggest that cost-effectiveness be used as a basis for decision rules. This paper presents an analytical framework that is familiar to clinicians and shows that medical necessity and cost-effectiveness do not provide deterministic rules for clinical decision making. First, 2 x 2 tables are used to show the tradeoff between the sensitivity and specificity of decision rules. Then, the example of asymptomatic abdominal aortic aneurysm is used to show that these tradeoffs can be seen as a continuum of decision rules on a receiver-operating characteristic curve. Society can therefore choose a decision threshold on the basis of medical necessity that optimizes the number of lives saved or any other desired outcome, but the tradeoff between sensitivity and specificity cannot be avoided. Applying cost-effectiveness criteria may change the decision threshold because cost-effectiveness itself involves inherent tradeoffs that create additional ambiguity for clinical decisions. The conclusion is that decision rules based on medical necessity or cost-effectiveness should not be considered deterministic. Rather, decision rules are useful when they make assumptions explicit and specify tradeoffs so that clinicians, patients, and payers can make better decisions.

Statistical expert systems for process control and error detection for VLSI

As chip densities increase the number of process parameters which require attention increases dramatically making the job of the process engineer exceedingly difficult. This paper will discuss the use of Statistical Process Control and Taguchi-type statistically designed experiments to develop decision rules for use in statistical expert systems. Statistical experiments, using Taguchi's method, have been carried out in the area of photolithography. This results in a basis for decision rules indicating the importance and effect of a given sub-process in photolithography. Using the results of Taguchi experiments and expert process knowledge, an inference can be drawn to lead the expert system to a diagnostic conclusion. The statistical expert system incorporates, diagnosis, design, planning and scheduling.