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Copyright of Journal of Health & Nursing Management / Sağlık ve Hemşirelik Yönetimi Dergisi is the property of Logos Medical Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Glioma grading is a critical procedure for selecting the most effective treatment policy. Biopsy result is the gold standard method for glioma grading, but inherent sampling errors in the biopsy procedure could lead to tumor misclassification. Aim: This study evaluated grading performances of a more comprehensive collection of the physiological indices quantified using an optimized dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) paradigm for glioma grading. Methods: Thirty-five patients with glioma underwent DCE-MR imaging to evaluate the grading performances of DCE-MRI-derived physiological indices. The statistical differences in the physiological indices between the different grades of gliomas were studied, and the grading performances of these parameters were evaluated using the leave-one-out cross-validation method. Results: There were significant statistical differences in DCE-MRI-derived physiological indices between the different grades of gliomas. The mean rCBVs for grade II (low-grade glioma, LGG), grade III, grade IV, and high-grade (HGG) gliomas were 2.03 ± 0.78, 3.61 ± 1.64, 7.14 ± 3.19, and 5.28 ± 3.02, respectively. The mean rCBFs of 1.94 ± 0.97, 2.67 ± 0.96, 4.57 ± 1.77, and 3.57 ± 1.68 were, respectively, quantified for grade II (LGG), grade III, grade IV, and high-grade gliomas. The leave-one-out cross-validation method indicates that the grades of glioma tumors could be determined based on a specific threshold for each physiological index; for example, the optimal cutoff values for rCBF, rCBV, Ktrans, Kep, and Vp indices to distinguish between HGGs and LGGs were 2.11, 2.80, 0.025 mL/g min, 0.29 min−1, and 0.065 mL/g, respectively. Conclusions: From the results, it could be concluded that glioma grades could be determined using DCE-MRI-derived physiological indices with an acceptable agreement with histopathological results. [ABSTRACT FROM AUTHOR]

Introduction/Objectives: The fecal immunochemical test (FIT) helps triage primary care patients at risk of colorectal cancer (CRC). Improving FIT returns has received recent attention, however uncertainty exists regarding the accurate completion of samples provided for laboratory analysis. This study aims to identify the rejection rate of returned FIT samples and determine rejection causes. Methods: FIT samples from symptomatic patients within South Yorkshire, Bassetlaw, and North Derbyshire are processed at a central laboratory. Tests requests are made from 225 GP practices, which serve an estimated 2 million population. This study describes a retrospective review of FIT samples received in the central laboratory between 01/09/19 and 31/12/22. Locally held data was interrogated in March 2023 to determine the number of FIT samples received and rejected during the study period. Documented reasons for rejection were explored to identify common themes. Results: Total FIT specimens received during the study period was 126 422. Of these, 5190 (4.1%) were rejected. Monthly rejection rates fell from 17.4% in September 2019 to 1.3% in December 2022 (P <.001). Sampling errors were the most frequent cause for FIT rejection (2151/5190), with other causes including: expired specimen; no sample collection date/ time, no request form, incomplete patient information and illegible handwriting. Conclusions: This is the first study exploring FIT rejection rates in symptomatic primary care patients, which shows improvements in rejection rates over time. Targeted interventions could improve rejection rates further, thereby reducing NHS resource use and costs and diagnostic delays. [ABSTRACT FROM AUTHOR]

Copyright of Texto & Contexto Enfermagem is the property of Universidade Federal de Santa Catarina, Programa de Pos-Graduacao de Enfermagem and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study develops a theoretical model for the costs of an exam as a function of its duration. Two kind of costs are distinguished: (1) the costs of measurement errors and (2) the costs of the measurement. Both costs are expressed in time of the student. Based on a classical test theory model, enriched with assumptions on the context, the costs of the exam can be expressed as a function of various parameters, including the duration of the exam. It is shown that these costs can be minimized in time. Applied in a real example with reliability.80, the outcome is that the optimal exam time would be much shorter and would have reliability.675. The consequences of the model are investigated and discussed. One of the consequences is that optimal exam duration depends on the study load of the course, all other things being equal. It is argued that it is worthwhile to investigate empirically how much time students spend on preparing for resits. Six variants of the model are distinguished, which differ in their weights of the errors and in the way grades affect how much time students study for the resit. [ABSTRACT FROM AUTHOR]

The p-value is often misunderstood and, for example, misinterpreted as a probability for the correctness of the null hypothesis. The aim of this article is to first explain the definition of the p-value. Determining the p-value requires knowledge of a probability function. Howan appropriate statistical model is selected and how the p-value is determined usingthis model, the null hypothesis and the empirical data is explained using the t-distribution. When interpreting the p-value obtained in this way, two incompatible statistical schools of thought are confronted: the orthodox Neyman-Pearson hypothesis test, which amounts to a decision between the null hypothesis and a complementary alternative hypothesis, and Fisher's significance test, in which no alternative hypothesis is formulated and in which the smaller the p-value, the greater the evidence against the null hypothesis. The amount ends with some critical remarks about the handling of p-values. [ABSTRACT FROM AUTHOR]

The article presents the role of using Hypothesis tests. Topics discussed Hypothesis tests used in statistics to assess a difference between two samples; highlights hypothesis tests that describes the effects of drugs, the comparison of the heights of two groups, and the methods to estimate population parameters; and mentions the null hypothesis.

Background: Extracellular volume fraction (ECV) is altered in pathological cardiac remodeling and predicts death and arrhythmia. ECV can be quantified using cardiovascular magnetic resonance (CMR) T1 mapping but calculation requires a measured hematocrit (Hct). The longitudinal relaxation of blood has been used in adults to generate a synthetic Hct (estimate of true Hct) but has not been validated in pediatric populations. Methods: One hundred fourteen children and young adults underwent a total of 163 CMRs with T1 mapping. The majority of subjects had a measured Hct the same day (N = 146). Native and post-contrast T1 were determined in blood pool, septum, and free wall of mid-LV, avoiding areas of late gadolinium enhancement. Synthetic Hct and ECV were calculated and intraclass correlation coefficient (ICC) and linear regression were used to compare measured and synthetic values. Results: The mean age was 16.4 ± 6.4 years and mean left ventricular ejection fraction was 59% ± 9%. The mean measured Hct was 41.8 ± 3.0% compared to the mean synthetic Hct of 43.2% ± 2.9% (p < 0.001, ICC 0.46 [0.27, 0.52]) with the previously published model and 41.8% ± 1.4% (p < 0.001, ICC 0.28 [0.13, 0.42]) with the locally-derived model. Mean measured mid-free wall ECV was 30.5% ± 4.8% and mean synthetic mid-free wall ECV of local model was 29. 7% ± 4.6% (p < 0.001, ICC 0.93 [0.91, 0.95]). Correlations were not affected by heart rate and did not significantly differ in subpopulation analysis. While the ICC was strong, differences between measured and synthetic ECV ranged from -8. 4% to 4.3% in the septum and -12.6% to 15.8% in the free wall. Using our laboratory's normal cut-off of 28.5%, 59 patients (37%) were miscategorized (53 false negatives, 6 false positives) with published model ECV. The local model had 37 miscategorizations (20 false negatives, 17 false positives), significantly fewer but still a substantial number (23%). Conclusions: Our data suggest that use of synthetic Hct for the calculation of ECV results in miscategorization of individual patients. This difference may be less significant once synthetic ECV is calculated and averaged over a large research cohort, making it potentially useful as a research tool. However, we recommend formal measurement of Hct in children and young adults for clinical CMRs. [ABSTRACT FROM AUTHOR]

The objectives of this study were to determine the sample size, in terms of number of plants, needed to estimate the average values of productive traits of the pigeon pea and to determine whether the sample size needed varies between traits and between crop years. Separate uniformity trials were conducted in 2011/2012 and 2012/2013. In each trial, 360 plants were demarcated, and the fresh and dry masses of roots, stems, and leaves and of shoots and the total plant were evaluated during blossoming for 10 productive traits. Descriptive statistics were calculated, normality and randomness were checked, and the sample size was calculated. There was variability in the sample size between the productive traits and crop years of the pigeon pea culture. To estimate the averages of the productive traits with a 20% maximum estimation error and 95% confidence level, 70 plants are sufficient. [ABSTRACT FROM AUTHOR]