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Statements such as 'trend towards' and 'tended to' in regards to 'statistical significance' are ambiguous and reflect the continued focus on proximity of p‐values to.05. By themselves, p‐values do not provide a measure of effect size or other information needed to judge clinical importance. The goal of this study was to examine original research articles in the journal Veterinary and Comparative Oncology over 2 years, and describe the use of 'trend' statements. Articles were reviewed for 'trend' and 'tended to' statements, the number of statements made per paper, whether a p‐value and/or 95% confidence interval was provided to accompany these statements, and what the p‐value was (if provided). We noted 15.8% of articles included at least one 'trend' or 'tended to' statement to describe differences between groups. Specifically, 10.5% of articles used these statements to describe differences without providing the associated p‐value, or where p >.05. Of the total 36 'trend' statements noted, six were not accompanied by p‐values and eight were associated with p‐values >.10. Similarly, four of the 16 'tended to' statements were not accompanied by p‐values, and one was associated with a p‐value >.10. These data reveal a similar emphasis on p‐value proximity in veterinary oncology literature compared to that of human oncology literature. Furthermore, these findings highlight the ambiguity of 'trend' statements and support the development of additional guidelines (e.g., the de‐emphasis of p‐value proximity to an arbitrary threshold, and effect size reporting) for interpreting results in veterinary oncology research. [ABSTRACT FROM AUTHOR]

Background and objective A rough estimate indicated that use of samples of size not larger than ten is not uncommon in biomedical research and that many of such studies are limited to strong effects due to sample sizes smaller than six. For data collected from biomedical experiments it is also often unknown if mathematical requirements incorporated in the sample comparison methods are satisfied. Methods Computer simulated experiments were used to examine performance of methods for qualitative sample comparison and its dependence on the effectiveness of exposure, effect intensity, distribution of studied parameter values in the population, and sample size. The Type I and Type II errors, their average, as well as the maximal errors were considered. Results The sample size 9 and the t - test method with p  = 5% ensured error smaller than 5% even for weak effects. For sample sizes 6–8 the same method enabled detection of weak effects with errors smaller than 20%. If the sample sizes were 3–5, weak effects could not be detected with an acceptable error; however, the smallest maximal error in the most general case that includes weak effects is granted by the standard error of the mean method. The increase of sample size from 5 to 9 led to seven times more accurate detection of weak effects. Strong effects were detected regardless of the sample size and method used. Conclusions The minimal recommended sample size for biomedical experiments is 9. Use of smaller sizes and the method of their comparison should be justified by the objective of the experiment. [ABSTRACT FROM AUTHOR]

Background: Direct measurement of insulin sensitivity in children with type 1 diabetes is cumbersome and time consuming. Objective: The aim of our study was to develop novel, accurate machine learning‐based methods of insulin resistance estimation in children with type 1 diabetes. Methods: A hyperinsulinemic hyperglycemic clamp study was performed to evaluate the glucose disposal rate (GDR) in a study group consisting of 315 patients aged 7.6 to 19.7 years. The group was randomly divided into a training and independent testing set for model performance assessment. GDR was estimated on the basis of simple clinical variables using 2 non‐linear methods: artificial neural networks (ANN) and multivariate adaptive regression splines (MARSplines). The results were compared against the most frequently used predictive model, based on waist circumference, triglyceride (TG), and HbA1c levels. Results: The reference model showed moderate performance ( R 2 = 0.26) with a median absolute percentage error of 49.1%, and with the worst fit observed in young (7‐12 years) children ( R 2 = 0.17). Predictions of the MARSplines model were significantly more accurate than those of the reference model (median error 3.6%, R 2 = 0.44 P < .0001). The predictions of the ANN, however, showed significantly lower error than those of the reference model (P < .0001) and MARSplines (P < .0001) and better fit regardless of patient age. ANN‐estimated GDRs were within a ±20% error range in 75% of cases with a median error of 0.6% and an R 2 = 0.66. The predictive tool is available at http://link.konsta.com.pl/gdr. Conclusions: The developed GDR estimation model reliant on ANN allows for an optimized prediction of GDR for research and clinical purposes. [ABSTRACT FROM AUTHOR]

The study is a retrospective analysis of data obtained from a cardiovascular disease prevention program financed by the National Health Fund (Poland). The aim of the study was to evaluate the population to demonstrate the prevalence of favourable and unfavourable lipid parameters and blood pressure values depending on age and BMI. A total of 2,616 subjects were included in the study (811 men and 1805 women aged between 35 and 55 years of age) who perceived themselves as completely healthy individuals and in whom no cardiovascular disease or diabetes mellitus had been diagnosed. We evaluated blood pressure values, body weight, height, BMI, fasting glucose, total cholesterol, triglycerides, HDL and LDL in the serum of venous blood. The above-mentioned parameters were compared in women and men depending on BMI and age. It was demonstrated that the epidemiological situation of women in the analysed age group, regardless of the studied parameter and method of its evaluation, is much more favourable than that of men. We have demonstrated that evaluation of the analysed lipid parameters and blood pressure should be performed with consideration to gender and age. Otherwise conclusions may be obtained which are not satisfied by 75-95% of the population in a given age group. Obesity was highlighted as a factor triggering further lipid disturbances and blood pressure increase. [ABSTRACT FROM AUTHOR]

The article presents a study on the use of statistics and biomedical informatics in forensic investigation. It explains that biomedical informatics has been in use in the field of dentistry and in calculating weight-of-evidence for forensic DNA profile identification. There is a tool in forensic dentistry that can collect DNA and dental evidence called Multimedia Distributed Record (MUDR) and Electronic Health Record (EHR) with Dent Cross component which can identify a person based on methods of probability and statistics.

Cholangiocellular cancer (CCC) is a malignant neoplasm of the hepatobiliary system that is difficult to diagnose and treat. Currently, the most effective treatment of CCC is demonstrated under the control by fluorescent diagnosis. Photodynamic therapy (PDT) has also shown good results in the treatment of this disease, and fluorescence analysis of the photosensitizer is a good approach to control PDT. This article presents the results of a comparison of spectroscopic and quantitative video-fluorescent analysis of chlorin e6 photosensitizer fluorescence in vivo during cholangiocellular cancer surgery. Spectroscopic analysis provides accurate information about the concentration of the photosensitizer in the tumor, while the video-fluorescence method is convenient for visualizing tumor margins. A direct correlation is shown between these two methods when comparing the fluorescence signals before and after PDT. The applied paired Student's t-test shows a significant difference between fluorescence signal before and after PDT in both diagnostic methods. In this regard, video-fluorescence navigation is not inferior in accuracy, sensitivity, or efficiency to spectroscopic methods. [ABSTRACT FROM AUTHOR]