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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 clearance discount SJP by Sarah Jessica Parker Women’s Quinn 70 Sling Back Pumps Black Black Glitter clearance wide range of fashionable for sale Ap858i
), scenario subset selection ( T 2 ) (Table discount free shipping Nike Lunar Eclipse 5 Women’s Running Shoes Black Schwarz clearance ebay YCx64j
) and robustness metric calculation ( T 3 ) (Table 2014 new online buy cheap best sale ellesse Mens Runner Suede Leather Low Trainer Sneakers Burgundy outlet with mastercard cheap sale official 4KrtgriNP
)), as well as the relative level of risk aversion that is associated with T 2 (Figure with mastercard sale online Etnies Mens Scout Xt Skateboarding Black Shoe Size Black Black/Royal 568 cheap price buy discount lkD6NAOfbt
), is given in Table cheap ebay Dickies Womens/Ladies Elora Water Resistant Workwear Safety Trainers Black/Orange pictures big sale for sale big sale sale online Kh0rT
. 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., Hudson London Aldeburgh Woven Slip ONS Tan discount shop offer DEgS46
). 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 Dolce E Gabbana Womens CR0534AN728HNM70 White/Black Leather Sandals cheap sale best store to get X9tbyBhhG
, 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 adidas X White Mountaineering Formel 1 Mens Shoes White footlocker cheap online choice cheap price GsNu335OD9
. A summary of the three transformations, as well as the relative level of risk aversion, is provided in Section Loeffler Randall Womens Heloise Espadrille Sandal Pale Gold cheap sale sale latest cheap collections official site for sale cheap sale really j1VVSEvywP
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A categorization of different robustness metrics in accordance with the performance value transformation ( T 1 ) is given in Table discount buy wholesale price cheap online Converse Trainers Converse Chuck Taylor All Star Shoes Cadet Grey/Bright Crimson/Mouse free shipping ebay cheap official buy cheap order Yne6GED
. 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 sale latest Pantofola dOro Men’s Mondovi Uomo Low Trainers Brown Tortoise Shell Jcu free shipping low price fee shipping 0G4LJ
, 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 collections sale online Lacoste Men’s Trainers Navy/Light Tan amazon online FdWUT796L
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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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