S1 File: Supporting tables. Table A in S1 File.

1. Expert Choice V11 Executive Orders
2. Expert Choice Model

Data on objectives for treatment alternatives. Table B in S1 File. Relative differences between objectives at third level of hierarchy. Table C in S1 File.

Treatment priorities by objectives. Table D in S1 File. Relative differences between alternatives for maximizing reduction of HbA1c.

Table E in S1 File. Relative differences between alternatives for minimizing risk of fracture. Table F in S1 File. Relative differences between alternatives for minimizing weight gain.

Table G in S1 File. Relative differences between alternatives for minimizing GI symptoms. Table H in S1 File. Relative differences between alternatives for minimizing risk of severe hypoglycemia. Table I in S1 File.

Expert Choice V11 Executive Orders

Relative differences between alternatives for minimizing risk of CHF. Table J in S1 File. Relative differences between alternatives for minimizing risk of acute pancreatitis. Table K in S1 File.

Relative differences between treatment alternatives for minimizing risk of bladder cancer.(DOC). S2 File: Supporting Figures. Fig A in S2 File. Process used for development and conduct of the Analytic Hierarchy Process. Fig B in S2 File Group Session Feedback Questions. Fig C in S2 File. Final Analytic Hierarchy Process Model.

Fig D in S2 File. Analytic Hierarchy Process Case Scenario. Fig E in S2 File. Example of interface used to obtain user input on relative importance of clinical differences in HbA1c-lowering for treatment alternatives. Fig F in S2 File. Overall global priorities when maximizing benefit completely over minimizing harm. Fig G in S2 File.

Overall global priorities when minimizing harms completely over maximizing benefits.(DOCX). MethodsWe conducted an AHP with nine diabetes experts using structured interviews to rank add-on therapies (to metformin) for type 2 diabetes.

During the AHP, participants compared treatment alternatives relative to eight outcomes (hemoglobin A1c-lowering and seven potential harms) and the relative importance of the different outcomes. The AHP model and instrument were pre-tested and pilot-tested prior to use. Results were discussed and an evaluation of the AHP was conducted during a group session. We conducted the quantitative analysis using Expert Choice software with the ideal mode to determine the priority of treatment alternatives.

ResultsParticipants judged exenatide to be the best add-on therapy followed by sitagliptin, sulfonylureas, and then pioglitazone. Maximizing benefit was judged 21% more important than minimizing harm.

Minimizing severe hypoglycemia was judged to be the most important harm to avoid. Exenatide was the best overall alternative if the importance of minimizing harms was prioritized completely over maximizing benefits. Participants reported that the AHP improved transparency, consistency, and an understanding of others’ perspectives and agreed that the results reflected the views of the group. IntroductionWhile metformin is the clear first-line medication for the pharmacologic treatment of type 2 diabetes , the choice of add-on medications is vast with 11 additional classes available ,. Development and refinement of the AHP modelWe used standard AHP methodology to define and refine the decision context, treatment alternatives, and objectives (i.e., treatment-related benefits and harms) for the AHP model ,. The decision context consisted of the specific decision goal, the relevant population and the decision-makers, and we limited the options to pharmacologic treatment alternatives. We constructed the AHP model as a hierarchy with the decision goal at the top and treatment alternatives at the bottom.

The level of the hierarchy below the decision goal comprised the general objectives with more specific objectives placed below the general objectives. We aimed for seven or fewer objectives on a given level to reduce the number of comparisons and thus reduce respondent burden and improve consistency.

We framed objectives positively (e.g., “maximize benefit” and “minimize harms”). At each level of the hierarchy, objectives are compared to one another in a pairwise fashion to determine their importance weights.

Treatment alternatives are then compared to one another in a pairwise fashion taking into consideration their relative ability to fulfill the criteria; these results are termed “judgments”.We used the most current FDA label information for each treatment alternative to obtain data on the objectives and supplemented this with results from a Comparative Effectiveness Review of diabetes medications developed under contract from the Agency for Healthcare Quality and Research (Table A in ). We evaluated multiple visual representations of the treatment-specific quantitative evidence based on prior work and selected bar charts for use during the actual AHP sessions. We presented data on objectives with either metformin or placebo/usual care as the reference depending on the availability of data. Development and refinement of the AHP instrumentThe AHP hierarchy was entered into Expert Choice, a widely-used software package, which has a web-based platform and can perform analyses in real time. This process translated the hierarchy into a series of questions asking participants to judge the relative weights of alternatives and objectives in relation to the objectives just above them in the hierarchy ,. The wording and presentation of questions was customized for this application, including the presentation of information for the participants that described the decision context and the hierarchical model, and a brief explanation of the AHP method.We validated and refined the decision context, model content, and hierarchy through in-person group sessions with our panel of experts on two occasions (pilot sessions lasted 60–90 minutes). During the pilot, expert participants made comparisons among the alternatives or objectives at each level of the hierarchy by entering direct numeric weights (numbers between 0 and 1) which were then transformed by the software to the usual AHP scale using the standard eigenvector procedure ,.

Expert Choice Model

Conduct and analysis of the AHPFor the conduct of the final AHP, we sent participants a web link to the AHP instrument with instructions to complete it prior to a consensus group session. Relative weights from pairwise comparisons of objectives were obtained by calculation of the right principal eigenvector of the relevant matrix (e.g., matrix of the pairwise comparisons between objectives at one level of the hierarchy). Expert Choice uses the matrix multiplication method, considered to be accurate, for this calculation. We used the ideal synthesis mode which is designed to identify the single best alternative or most important criterion.

An advantage of this mode is that relative ranks are preserved in the case of the addition or removal of an 'irrelevant' alternative. Similar calculations were performed to obtain weights for treatment-specific evidence on the objectives.The priority of a given treatment alternative with respect to meeting an objective at the next level up in the hierarchy was obtained by summing the products of the weight for the alternative with respect to the objective and each objective weight at the level below in the hierarchy. Priorities for alternatives were compared using ratios with relative differences of 1.1 considered significant according to standard AHP criteria. A ratio of 1.1 between two alternatives implies a 10% multiplicative difference with respect to how the alternatives meet a given objective at the next level above in the hierarchy. Priorities for objectives were calculated and interpreted similarly. Group priorities were calculated using the geometric mean of the individual experts’ priorities.We performed sensitivity analyses to understand the impact of particular objectives and weights on our results: 1) Increasing the priority of maximizing benefits to 100%; 2) Increasing the priority of minimizing harms to 100%; and 3) Conducting the analysis in the distributive mode. In contrast to the ideal mode, the distributive mode produces results that evaluate alternatives or criteria proportionately.

This characteristic makes the distributive mode more suitable for identifying relative priorities among criteria or alternatives but also makes results dependent on the composition of the set of alternatives or criteria being compared.We anticipated heterogeneity in weights of objectives and alternatives across participants and evaluated standard deviations (indicating the extent of agreement or disagreement) for weights.We used the consistency index to evaluate consistency, or transitivity, of weights. A perfectly consistent set of comparisons has a consistency index of 0. Based on generally accepted convention, we considered weights to be inconsistent if consistency index values exceeded 0.15. ResultsGroup priority scores for all objectives are shown in. Overall, maximizing benefits was judged to be 21% more important than minimizing harm. Reducing HbA1c was judged to be 3.71 and 1.80 times more important than minimizing non-serious and serious harms, respectively, and minimizing serious harms was more than twice as important as minimizing non-serious harms (Table A and Table B in ). Relative differences between objectives at the lowest level of the hierarchy are shown in.

1.Executive Order #54 requires that UW System employees report instances of: A. Child Abuse B. Both of the above2.UW System employees include: A. Faculty and Staff B.

Student Employees D. A and C3.Child abuse can be defined as: A. Physical abuse B. Sexual exploitation C. Emotional damage D.

All of the above4.Warning signs of neglect may include: A. Inadequate clothing B. Withdrawal from others C. All of the above5.Warning signs of emotional damage may include: A. Feelings of little self-worth B. Chota bheem aur krishna game free download for pc.

Cruelty to animals C. Drop in school performance D.

All of the above6.You should make a report if you (in the course of employment): A. Observe an incident or threat of child abuse or neglect B.

Learn of an incident or threat of a child abuse or neglect C. Have reasonable cause to believe that child abuse or neglect has occurred or will occur D.

All of the above7.The employee making the report should: A. Wait 24 hours before making the report B. Make the report immediately C. Ask the child's parents before making the report D.

Get the child's permission before making the report8.To make a report, you should: A. Send an email to either the Department of Health and Human Services; the Superior Police Department; the Douglas County Sheriff's Department or UW-Superior Public Safety B. Talk with either the Department of Health and Human Services; the Superior Police Department; the Douglas County Sheriff's Department or UW-Superior Public Safety in person or on the phone C.

Write a letter to either the Department of Health and Human Services; the Superior Police Department; the Douglas County Sheriff's Department or UW-Superior Public Safety9.The guardian of the child that the report was made on will be notified who made the report: A. False10.If you aren't sure if you should make a report, you should: A. Not make the report B. Ask Public Safety C. Tell a co-worker about the situation11.If I make a report in good faith, I can be disciplined or terminated if it was found that abuse was not occurring: A.

False12.If I am aware of abuse, but don't report it, I could be convicted of a criminal misdemeanor and be subject to fines: A.