Then, in 3 experiments using dot lattices, they showed that the strength of the Gemcitabine conjoint effect of 2 grouping principles-grouping by proximity and grouping by similarity-is

equal to the sum of their separate effects. They propose a physiologically plausible model of this law.”
“Purpose: We determined the efficacy of onabotulinumtoxinA for neurogenic detrusor overactivity secondary to spinal cord injury or multiple sclerosis.

Materials and Methods: In a prospective, double-blind, multicenter study 57 patients 18 to 75 years old with neurogenic detrusor overactivity secondary to spinal cord injury or multiple sclerosis and urinary incontinence (defined as 1 or more occurrences daily) despite current antimuscarinic treatment were randomized to onabotulinumtoxinA 300 U (28) or placebo (29) via cystoscopic injection

at 30 intradetrusor sites, sparing the trigone. find more Patients were offered open label onabotulinumtoxinA 300 U at week 36 and followed a further 6 months while 24 each in the treatment and placebo groups received open label therapy. The primary efficacy parameter was daily urinary incontinence frequency on 3-day voiding diary at week 6. Secondary parameters were changes in the International Consultation on Incontinence Questionnaire and the urinary incontinence quality of life scale at week 6. Diary and quality of life evaluations were also done after open label treatment.

Results: The mean daily frequency of urinary incontinence episodes was significantly lower for onabotulinumtoxinA than for placebo at week 6 (1.31 vs 4.76, p <0.0001), and for weeks 24 and 36. Improved urodynamic and quality of life parameters for treatment vs placebo were evident at week 6 and persisted to weeks 24 to 36. The most common adverse event in each Immune system group was urinary tract infection.

Conclusions: In adults with antimuscarinic refractory neurogenic detrusor

overactivity and multiple sclerosis onabotulinumtoxinA is well tolerated and provides clinically beneficial improvement for up to 9 months.”
“Diagnostic hypothesis-generation processes are ubiquitous in human reasoning. For example, clinicians generate disease hypotheses to explain symptoms and help guide treatment, auditors generate hypotheses for identifying sources of accounting errors, and laypeople generate hypotheses to explain patterns of information (i.e., data) in the environment. The authors introduce a general model of human judgment aimed at describing how people generate hypotheses from memory and how these hypotheses serve as the basis of probability judgment and hypothesis testing. In 3 simulation studies, the authors illustrate the properties of the model, as well as its applicability to explaining several common findings in judgment and decision making, including how errors and biases in hypothesis generation can cascade into errors and biases in judgment.