Iowa GED Statistical Report, 2002

The following information summarizes the major statistical trends relative to Iowa’s GED testing program for calendar year 2002

Iowa GED Statistical Report, 2006

The following information summarizes the major statistical trends relative to Iowa’s GED testing program for calendar Year 2005.

Precision of a new tool to measure visceral adipose tissue (VAT) using dual-energy X-Ray absorptiometry (DXA).

OBJECTIVE: A new tool to quantify visceral adipose tissue (VAT) over the android region of a total body dual-energy x-ray absorptiometry (DXA) scan has recently been reported. The measurement, CoreScan, is currently available on Lunar iDXA densitometers. The purpose of the study was to determine the precision of the CoreScan VAT measurement, which is critical for understanding the utility of this measure in longitudinal trials. DESIGN AND METHODS: VAT precision was characterized in both an anthropomorphic imaging phantom (measured on 10 Lunar iDXA systems) and a clinical population consisting of obese women (n = 32). RESULTS: The intrascanner precision for the VAT phantom across 9 quantities of VAT mass (0-1,800 g) ranged from 28.4 to 38.0 g. The interscanner precision ranged from 24.7 to 38.4 g. There was no statistical dependence on the quantity of VAT for either the inter- or intrascanner precision result (p = 0.670). Combining inter- and intrascanner precision yielded a total phantom precision estimate of 47.6 g for VAT mass, which corresponds to a 4.8% coefficient of variance (CV) for a 1 kg VAT mass. Our clinical population, who completed replicate total body scans with repositioning between scans, showed a precision of 56.8 g on an average VAT mass of 1110.4 g. This corresponds to a 5.1% CV. Hence, the in vivo precision result was similar to the phantom precision result. CONCLUSIONS: The study suggests that CoreScan has a relatively low precision error in both phantoms and obese women and therefore may be a useful addition to clinical trials where interventions are targeted towards changes in visceral adiposity.

A-1 Monthly Public Assistance Statistical Report Family Investment Program, December 2006

A-1 Monthly Public Assistance Statistical Report Family Investment Program.

A-1 Monthly Public Assistance Statistical Report Family Investment Program, January 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program for January 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, February 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program - February 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, March 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - March 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, April 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - April 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, May 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - May 2007

Strong minimax lower bounds for learning

Minimax lower bounds for concept learning state, for example, thatfor each sample size $n$ and learning rule $g_n$, there exists a distributionof the observation $X$ and a concept $C$ to be learnt such that the expectederror of $g_n$ is at least a constant times $V/n$, where $V$ is the VC dimensionof the concept class. However, these bounds do not tell anything about therate of decrease of the error for a {\sl fixed} distribution--concept pair.\\In this paper we investigate minimax lower bounds in such a--stronger--sense.We show that for several natural $k$--parameter concept classes, includingthe class of linear halfspaces, the class of balls, the class of polyhedrawith a certain number of faces, and a class of neural networks, for any{\sl sequence} of learning rules $\{g_n\}$, there exists a fixed distributionof $X$ and a fixed concept $C$ such that the expected error is larger thana constant times $k/n$ for {\sl infinitely many n}. We also obtain suchstrong minimax lower bounds for the tail distribution of the probabilityof error, which extend the corresponding minimax lower bounds.

A-1 Monthly Public Assistance Statistical Report Family Investment Program, June 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - June 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, July 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - July 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, August 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - August 2007

L'erreur en médecine ambulatoire: comment l'aborder. [Error in ambulatory medicine: how to deal with it?]

Medical errors compromise patient safety in ambulatory practice. These errors must be faced in a framework that reduces to a minimum their consequences for the patients. This approach relies on the implementation of a new culture without stigmatization and where errors are disclosed to the patients; this culture implies the build up of a system for reporting errors associated to an in-depth analysis of the system, looking for root causes and insufficient barriers with the aim to fix them. A useful education tool is the "critical situations" meeting during which physicians are encouraged to openly present adverse events and "near misses". Their analysis, with supportive attitude towards involved staff members, allows to reveal systems failures within the institution or the private practice.