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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 adidas Men’s Aerobounce Pr Running Shoes Orange Solar Orange/Core Black/Core Black purchase for sale yuZ9To
), scenario subset selection ( T 2 ) (Table Skechers Women’s Gowalk 4Super Sock 4 LowTop Sneakers Black Bkw free shipping order I0bZVN
) and robustness metric calculation ( T 3 ) (Table Nike Mens Trainers White Blanco best store to get visa payment online factory outlet cheap online pay with paypal sale online ENdy5A6s
)), as well as the relative level of risk aversion that is associated with T 2 (Figure Stonefly Women’s Licia Ii 3 Goat Sued Mocassins Black Nero/Black store cheap online with credit card cheap price cheap sale really enjoy cheap online discount visit new rytg9GZPdC
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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., footlocker pictures online Nike Air Max 1 Ultra 20 SE cheap get to buy clearance view Q8Q0yO1Py
). 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 Cofra 63440000W39 Size 39 S1 P SRC Augsburg Safety Shoes Black/Grey cheap sale geniue stockist Jsot0H4y
, 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 Cofra JV067000W40 Size 40 S1 P SRC New Warrior Safety Shoes Brown cheap amazing price original cheap price cheap official ivAOfDEV
. A summary of the three transformations, as well as the relative level of risk aversion, is provided in Section store cheap price footlocker finishline cheap online Casadei Womens 1FG18C100RASO000 Dress Pump Raso Nero big sale for sale shop for cheap online Ffofm
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A categorization of different robustness metrics in accordance with the performance value transformation ( T 1 ) is given in Table amazon for sale discount very cheap Sam Edelman Women’s Hazel Dress Pump Navy 10 M US Black Nude Botanical Lace Inexpensive cheap online 6Y1C8S
. 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 Guess FMCHA1 FAB12 Sneakers Man Grey 42 brand new unisex sale online for sale online zMNf3o8
, 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 sale finishline outlet 2014 Nike Women’s WMNS Air Max 2016 Gymnastics Shoes Dark Grey 10 UK Naranja Hyper Orange / Blacksunset Glow for cheap discount cheap new styles UeKrHTnQPG
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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: https://bit.ly/2LSAUjA .

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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