be tailored based on clinical indications to ensure that CT radiation dose to pediatric patients is appropriate [4–8]. Several studies during the last half of a century have tried to assess image quality objectively [9–13]. Parameters like quantitative image noise, signal-to-noise ratio, contrast-to- noise ratio, modulation transfer function (MTF), normal- ized noise power spectrum (NNPS), detective quantum efficiency (DQE), contrast details, and forced-choice just noticeable difference (JND) have been studied. Most ob- jective scoringcriteria are useful for academic studies ra- ther than for applying in day-to-day practice in clinical situations globally. The results show that a particular image quality parameter is sensitive in detecting a specific aspect of information in the image but inadequate as an overall measure of image quality in terms of clinical usefulness. Subjective scoring systems for image quality and some using image noise, contrast, sharpness, and artifacts have been applied to CT image quality [13–16]. Such studies focus on the visual aspect of quality. The subjective judg- ment of images by a radiologist has been used routinely in day-to-day life, and it does provide a powerful tool to judge many aspects of image quality and information content simultaneously as needed for diagnosis. Some papers have compared the subjective scoring of images with objective indices and have confirmed the usefulness of subjective scoring [9, 10]. Generally, individual preferences of images ’ quality acceptability lead to variability in image quality and radiation dose. The interobserver variability is considered a substantial source of the problem that leads to inconsistent and inadequate subjective image quality assessments. If the criteria developed are such that they have a lower interob- server variability, it may become widely acceptable.
All of the pipelines with the new interaction scoring protocols in CANDO v1.5 yield promising benchmark performance. However, there is some variance depending on how many top putative drug candidates are generated and benchmarked: At the lowest cutoff (top10 putative drug candidates), the pipeline with the best performance uses only the cheminformatics interaction score. At higher cutoffs (top25–top100), the pipeline with the best performance combines the bioinformatic and cheminfor- matics outputs for the interaction scores. These results help guide future experimental validation studies of the platform by enabling us to select the appropriate interac- tion scoring protocol based on the number of putative drug candidates to be tested.
The scoring of criteria was 0-4 in a five-point scale, based on the contents of the archive data. 0: no evidence to satisfy the criterion.1: limited, weak link to the Adult Education methodology, 2: moderate, 3: substantial, strong satisfaction, 4: the design and implementation of the program coincided with the Adult Education Programs’, so the program judged effective for the in-school teachers’ training in environmental issues. Recurring readings of AFs and FRs were focused on that phrases and points that answer the research questions and scored the criteria. Thereafter, the scoringcriteria entered into special databases in EXCEL, separately for the group A and B of the SYPs and in total of the Final Reports for statistical analysis per criterion. SYP-A and SYP-B compared with the p- values of the non-parametric test Wilcoxon & Mann- Whitney.Wherever the p-value is < 0.05, there is statistically significant difference.
Abstract. The tendering and evaluation of water conservancy projects is an important part of China's infrastructure construction, and the evaluation of bids is also a key link in the construction of water conservancy projects. In view of the problems in the bid evaluation process for project of water conservancy and hydropower in a certain province, the proportion of subjective elements is large, the proportion of bid evaluation factors is not fixed, the randomness is large, and the external obstacle factors are large. This paper proposes the use of network analysis in the bid evaluation process. The method (ANP) considers the mutual influence and dependence of each index, and establishes a multi-index comprehensive scoringcriteria model for the nonlinear combination of indexes. Based on the model establishment process, the algorithm of ANP evaluation model was implemented using C# and applied to the bid evaluation process, which made the evaluation process more scientific and rational.
Although many studies have previously evaluated HER2 status in GC, the patient cohorts and scoringcriteria have varied, resulting in discrepancies in HER2-positivity rates varying from about 4% to 53%, with a median rate of 18% . The ToGA study developed a new set of IHC scoringcriteria based on the study by Hofmann et al.  and found HER2-positive (defined as IHC 3+ or IHC 2+/FISH+) tumors in 16% of metastatic GC cases. The efficacy of trastuzumab for treating metastatic GC with HER2 overexpression demonstrated in the ToGA study is also promising for resectable HER2-positive gastric cancer. How- ever, few studies have been conducted to examine the fre- quency of HER2-positive tumors determined by the new criteria in resectable gastric cancer [20,21], especially in a large Chinese cohort. In this study, IHC analysis according to standardized scoringcriteria was used to assess the in- cidence of HER2-positivity in primary resected GC and GEJ cancer samples in a C9pt?>The relationship between HER2 overexpression and gene amplification was also examined in GC and GEJ adenocarcinoma.
After this examination of the CLA test, I think I have discovered a serious, perhaps fatal, weakness among its many strengths. Its goals are to be commended—measuring students’ higher-order skills of critical thinking, analytic reasoning, problem solving, and written communication—these are essential to higher learn- ing. And the rubrics (Benjamin et al., 2009, pp. 41-3) and the CLA ScoringCriteria (CAE, 2011a) used to assess the students’ application and enhancement of these higher-order skills are spot on. So, the graders seem to be looking for the right things in the students’ responses. But, according to my findings, they just aren’t correctly finding them very well. Remember, the critical- thinking criteria the graders are to use in scoring the students’ responses in Make-an-Argument are: “Clarifying a position and supporting it with evidence, considering alternative viewpoints or counter points to their argument, developing logical, persua- sive arguments, [and exhibiting] depth and complexity of think- ing about the issues raised in the prompt” (Benjamin et al., 2009, pp. 53-4). While obviously believing that these criteria are met, the graders are in fact falling for numerous informal falla- cies, platitudes, and evasions. They are being persuaded by ar- guments and criticisms that are simply not cogent. How can this be happening?!
The severity score for the laboratory parameters is based on the Common Terminology Criteria for Adverse Event (CTCAE) or similar scoringcriteria. The lowest CTCAE severity is grade 1 with the de ﬁ nition of “ mild; asymptomatic or mild symptoms; clinical or diagnostic observations only; intervention not indicated ” . Table 1 shows the CTCAE grade 1 criteria differences for some of the laboratory parameters between version 4.03 and version 5.0. 1,2 In version 4.03 the grade 1 criteria were based solely on the healthy volunteer reference range (HVRR) upper limits of normal (ULN). However, these de ﬁ nitions were modi ﬁ ed in CTCAE version 5.0 for the liver function laboratory parameters to also include fold change from baseline, if the baseline value (prior to subject getting the drug) was above the HVRR ULN. This change in the grade 1 criteria was to help identify potential clinical safety signals in individuals that had ele- vated liver function tests at baseline, such as patients with NASH or liver metastases. However, based on a pilot study recently reported, we know that liver function laboratory parameters are not the only laboratory parameters above the HVRR ULN at baseline, but rather approximately 25% of the laboratory parameters across various patient populations evaluated were above the HVRR ULN. 3
Select a point value to view scoringcriteria, solutions, and/or examples and to score the response. If the student uses incorrect results in a later step, the point can be earned for the later step. Note: It is not possible to determine the (or ) in one step because a weak acid is reacting with a strong base, yielding a basic solution. However, it is possible to calculate by imagining that and react completely to form a solution of pure , which then hydrolyzes to form and . From the calculation of , and can be determined.
This is one of the most widely used criteria in the area of accounting and finance for credit scoring applications in particular, and other fields, such as marketing and health in general. The average correct classification rate measures the proportion of the correctly classified cases as good credit and as bad credit in a particular data-set. The average correct classification rate is a significant criterion in evaluating the classification capability of the proposed scoring models. The idea of correct classification rates comes from a matrix, which is occasionally called “a confusion matrix", otherwise called a classification matrix, . A classification matrix presents the combinations of the number of actual and predicted observations in a data-set. In Yu  study, the confusion matrix was compared with another two criteria: Mahalanobis Distance and Kolmogorov-Smirnov Statistics with reference to ROC curve. In other studies this matrix has been compared with MSE and RMSE.
Implementation We implemented the neural baseline model with Keras and TensorFlow. The code will be made publicly available at an anony- mous URL once the paper is accepted. We chose the same hyperparameters and training settings as in Riordan et al. (2017)’s holistic scoring model. SVR Baseline We also implemented another simpler baseline model based on the support vec- tor regression model (SVR) following Sakaguchi et al. (2015) to provide sparse feature-based base- line results. We adopted the feature set proposed by Sakaguchi et al. (2015), which includes word 1- gram, word 2-gram, and predicate-argument struc- ture features 2 . We used KNP 4.16 (Kawahara and Kurohashi, 2006) to extract Japanese predicate- argument structure features.
Step 3 (selection of non-price criteria): The following criteria were identified to be most important in this comparison: Equivalence with the reference product, Macroeconomic benefit, Pharmacovigilance, Quality as- surance, Real world outcomes (clinical and economic), Reliability of drug supply, Stability and drug formula- tion, and Added value services related to product. These were selected from a list of criteria which have been pro- posed previously on an international level as most rele- vant base criteria in the comparison and evaluation of off-patent pharmaceuticals in developing countries . In addition, country of origin and package size were dis- cussed as potentially important decision criteria. How- ever, the participants were not able to form a consensus on objective and transparent performance measures for both criteria and therefore, both criteria were omitted from the further discussion. The participants concluded that country of origin is currently considered a substi- tute measure for quality, which in the new MCDA model is already addressed sufficiently by three of the eight base criteria (Equivalence with the reference prod- uct, Stability and drug formulation, Quality assurance). In relation to package size, the participants agreed that initially all comparisons should be made on equal units such as for example the defined daily dose. However, it