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20 September 2017
Added section on monitoring E.coli
6 July 2017
Added 'Guidance on the definition of healthcare associated Gram-negative bloodstream infections' in Essential Standards section.
3 May 2017
First published.
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An improvement resource to help health and social care economies reduce the number of Gram-negative bloodstream infections (BSIs) with an initial focus on Escherichia coli (E.coli).

The complete categorization of the commonly used robustness metrics considered in this article in accordance with the three transformations (performance value transformation ( T 1 ) (Table ASICS Sneaker HL7V41202 GelKayano Beige Beige fashionable UboE8
), scenario subset selection ( T 2 ) (Table Puma Women’s Defy WNs Fitness Shoes Black/White White Puma Whitepuma White 02 sale buy top quality sale online cheap sale outlet locations buy cheap finishline quality original YZ8mvn
) and robustness metric calculation ( T 3 ) (Table buy cheap buy Lemon Jelly Women’s Comfy Chelsea Boots Baby Blue Red Red 06 free shipping 2014 newest WmxtcX
)), as well as the relative level of risk aversion that is associated with T 2 (Figure Stonefly Women’s Dancy 1 Brush Off Chelsea Boots Black Nero/Black footaction online ML8KDJ2ZmI
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. It is hoped that this can provide some guidance to decision‐makers in relation to which robustness metric is appropriate for their decision context.

In relation to the performance value transformation ( T 1 ), which robustness metric is most appropriate depends on whether the performance value in question relates to the satisfaction of a system constraint or not, and is therefore a function of the properties of the system under consideration. For example, if the system is concerned with supplying water to a city, there is generally a hard constraint in terms of supply having to meet or exceeding demand, so that the city does not run out of water (Beh et al., cheap prices authentic Area Forte Ad1752 Women’s Sandals Grau Canguro Multi Grigio cheap supply clearance prices for nice for sale clearance best sale 296aJ7d
). The system performs satisfactorily if this demand is met and that is the primary concern of the decision‐maker. Alternatively, there might be a fixed budget for stream restoration activities, which also provides a constraint. In this case, a solution alternative performs satisfactorily if its cost does not exceed the budget. For the above examples, where performance values correspond to determining whether constraints have been met or not, satisficing metrics, such as Starr's domain criterion, are most appropriate.

In contrast, if the performance value in question relates to optimizing system performance, metrics that use the identity or regret transforms would be most suitable. For example, for the water supply security case mentioned above, the objective might be to identify the cheapest solution alternative that enables supply to satisfy demand. However, there might also be concern in over‐investment in expensive water supply infrastructure that is not needed, in which case robustness metrics that apply a regret transformation might be most appropriate, as this would enable the degree of over‐investment to be minimized when applied to the cost performance value. For the stream restoration example, however, decision‐makers might simply be interested in maximizing ecological response for the given budget. In this case, robustness metrics that use the identity transform might be most appropriate when considering performance values related to ecological response.

In relation to scenario subset selection ( T 2 ), which robustness metric is most appropriate depends on a combination of the likely impact of system failure and the degree of risk aversion of the decision‐maker. In general, if the consequences of system failure are more severe, the degree of risk‐aversion adopted would be higher, resulting in the selection of robustness metrics that consider scenarios that are likely to have a more deleterious impact on system performance. For example, in the water supply security case, it is likely that robustness metrics that consider more extreme scenarios would be considered, as a city running out of water would most likely have severe consequences. In contrast, as the potential negative impacts for the stream restoration example are arguably less severe, robustness metrics that use a wider range or less severe scenarios might be considered. However, this also depends on the values and degree of risk aversion of the decision maker.

The scenario subset selection transformation ( T 2 ) involves determining which values of f ( x i ,  S ) to use in the robustness metric calculation ( T 3 ) (i.e., f ( x i ,  S ) ⊆  f ( x i ,  S )), which is akin to selecting a subset of the available scenarios over which system performance is to be assessed. This reflects a particular degree of risk aversion, where consideration of more extreme scenarios in the calculation of a robustness metric corresponds to a higher degree of risk aversion and vice versa. As can be seen from Table Nike Mens Air Foamposite One Basketball Shoe clearance footaction GhVoDyO
, which scenarios are considered in the robustness calculation is highly variable between different metrics.

The third transformation ( T 3 ) involves the calculation of the actual robustness metric based on transformed system performance values ( T 1 ) for the selected scenarios ( T 2 ), which corresponds to the transformation of f ( x i ,  S ) to a single robustness value, R ( x i ,  S ). This equates to an identity transform in cases where only a single scenario is selected in T 2 , as there is only a single transformed performance value, which automatically becomes the robustness value. However, in cases where there are transformed performance values for multiple scenarios, these have to be transformed into a single value by means of calculating statistical moments of these values, such as the mean, standard deviation, skewness or kurtosis.

In this section, a taxonomy of different robustness metrics is given in accordance with the three transformations introduced in Section Vans Unisex Adults Authentic Brilliant Blue/True White Trainers Vnjv5C4 Brilliant Blue/True White buy cheap authentic shop offer cheap online 100% original for sale buy cheap buy clearance visa payment WaWwxfkJx
. A summary of the three transformations, as well as the relative level of risk aversion, is provided in Section cost for sale Asics Womens GelNimbus 20 Running Shoe Rose 7 BM US big sale cheap price free shipping lowest price outlet with paypal order online MfY84pXVZ

A categorization of different robustness metrics in accordance with the performance value transformation ( T 1 ) is given in Table lowest price for sale shopping online with mastercard Andrea Conti Women’s 0345735 Trainers Beige Beige 003 Qphok1zU
. As can be seen, the categorization is based on (1) whether calculation of a robustness metric is based on the absolute performance of a particular decision alternative or the performance of a decision alternative relative to that of another decision alternative or a benchmark; and (2) whether a robustness metric provides an indication of actual system performance or whether system performance is satisfactory compared with a pre‐specified performance threshold.

Many of the classic decision analytic robustness metrics belong to the bottom‐right hand quadrant of Table outlet store online visit cheap online Salewa Women’s Trailbreaker W GTX Fitness Shoes Multicolor Ocean/Malta free shipping comfortable free shipping visit new free shipping high quality y6ozbdsBfO
, including the maximax and maximin criteria, Hurwicz's optimism‐pessimism rule and Laplace's principle of insufficient reason, as well as well more recently developed metrics such as the mean‐variance criterion, percentile based skewness and percentile‐based peakedness. These metrics utilize information about the absolute performance (e.g., cost, reliability) of a particular decision alternative in a particular scenario. Consequently, values of f ( x i ,  S ) consist of these performance values, and robust decision alternatives are those that maximize system performance across the scenarios. The difference between these metrics is which values of the distribution of performance values over the different scenarios f ( x i ,  S ) they use in the robustness calculation (i.e., scenario subset selection ( T 2 )) and how these values are combined into a single value of R (i.e., robustness metric calculation ( T 3 )), as discussed in Sections adidas Unisex Kids Gazelle 2 Trainers Dark Grey White V cheap sale best seller URUZg7lfa
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Joe Garofoli is the San Francisco Chronicle’s senior political writer, covering national and state politics. He has worked at The Chronicle since 2000 and in Bay Area journalism since 1992, when he left the Milwaukee Journal. He is the host of “It’s All Political,” The Chronicle’s political podcast. Catch it here: .

He has won numerous awards and covered everything from fashion to the Jeffrey Dahmer serial killings to two Olympic Games to his own vasectomy — which he discussed on NPR’s “Talk of the Nation” after being told he couldn’t say the word “balls” on the air. He regularly appears on Bay Area radio and TV talking politics and is available to entertain at bar mitzvahs and First Communions. He is a graduate of Northwestern University and a proud native of Pittsburgh. Go Steelers!

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