Category: Statistics

Nurse Educ Today. 2021 Aug 28;107:105116. doi: 10.1016/j.nedt.2021.105116. Online ahead of print.

ABSTRACT

BACKGROUND: Physical examination and health assessment skills are essential components of nursing practice, and the critical elements to be taught merit further investigation.

OBJECTIVES: To develop and evaluate a physical examination and health assessment course based on a self-directed learning framework.

DESIGN: An action research design was employed.

SETTINGS: A baccalaureate nursing program of the university of science and technology in central Taiwan.

PARTICIPANTS: A convenience sample comprising 23 teaching faculty members and 41 enrolled second-year students was recruited.

METHODS: Structured questionnaires were developed for data collection. A paired t-test and the Kruskal-Wallis test were used for data analysis.

RESULTS: The course consisted of four parts: health history taking from a holistic perspective, examination skills in diverse systems, case exercise and discussion, and final objective structured clinical examination. Statistical significance was found in the areas of physical examination skills, critical thinking, and case analysis. Participants with mid-to high-level self-directed learning had significantly higher scores than those with low-level self-directed learning on physical examination skills and problem assessment. Internal and discriminant validity were supported.

CONCLUSION: The study results provide evidence supporting the use of self-directed learning framework in curriculum design. The course integrated necessary knowledge and skills enabled students to practice physical examination, and assessment skills may enhance student confidence in approaching patients in clinical encounters. However, the study was a descriptive design. The generalization of the results needs to be further validated by an experimental study.

PMID:34481313 | DOI:10.1016/j.nedt.2021.105116

Public Health. 2021 Aug 4;198:208-210. doi: 10.1016/j.puhe.2021.07.041. Online ahead of print.

ABSTRACT

OBJECTIVES: The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) leading to coronavirus disease 2019 (COVID-19) in China at the end of 2019 has resulted in a global pandemic. On 11 March 2020, the first case of COVID-19 was reported in Turkey. The aim of this study was to evaluate SARS-CoV-2 Real-Time Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) test results from the Medical Microbiology Laboratory of a pandemic hospital according to demographic data.

STUDY DESIGN: Retrospective cohort study.

METHODS: SARS-CoV-2 RT-PCR test results of 413,013 samples from 194,062 patients were retrospectively analysed. Tests were carried out between 27 March and 31 December 2020 using two commercial kits. The patient’s age and gender were recorded, in addition to the percentage of positive test results per month (i.e. monthly positivity). Pearson’s Chi-squared test was used to analyse statistical significance.

RESULTS: Overall SARS-CoV-2 positivity in the pandemic hospital was 19.9%. Female gender and younger age (0-18 years) had a statistically significant higher positivity (P < 0.05). There was a statistically significant higher positivity in August and September.

CONCLUSIONS: Higher positivity among the younger population and females may be the leading cause of low COVID-19 mortality rates in Turkey as these population groups are less likely to die from the disease. Governments should disaggregate COVID-19 data by age and gender, and vaccine studies focussing on younger populations should be accelerated because this population group represents an important source of infection.

PMID:34481276 | DOI:10.1016/j.puhe.2021.07.041

Nutrition. 2021 Jul 28;93:111425. doi: 10.1016/j.nut.2021.111425. Online ahead of print.

ABSTRACT

OBJECTIVES: Youngsters who are overweight or obese (YOO) have become an important global health concern. Some micronutrients may be modifiable influential factors. This study aimed to investigate the individual and joint association of whole-blood magnesium (WBMg) and total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), or high-density lipoprotein cholesterol (HDL-C) in YOO.

METHODS: This is a propensity score matching-based case-control study. YOO was defined depending on age- and sex-specific body mass index z-score, calculated with SAS macros (%group_standard and %WHO2007) from the World Health Organization website. WBMg, blood lipids, and covariates were carefully measured by trained technicians using a whole-blood, five-element, basic analyzer and atomic absorption spectrometer or automatic biochemical analyzer. Locally weighted scattered plot smoothing and multivariable conditional logistic regression models were applied to estimate the associations of WBMg and blood lipids in YOO.

RESULTS: WBMg was positively associated with YOO. The adjusted likelihood of YOO significantly increased by 21% (odds ratio: 1.21; 95% confidence interval [CI], 1.10-1.33) with per-interquartile range elevation of WBMg. Compared with the 1st quartile, adjusted odds ratios among youngsters in the 2^nd, 3^rd, and 4^th quartiles of WBMg were 1.11 (95% CI, 0.92-1.35), 1.29 (95% CI, 1.06-1.57), and 1.47 (95% CI, 1.18-1.83), respectively. Furthermore, the relationship between WBMg and YOO was moderated by lipid profiles. Compared with those having lower (< median) WBMg and TC, TG, LDL-C, or higher (≥ median) HDL-C, youngsters with both higher WBMg and TC, TG, LDL-C, or lower HDL-C had higher YOO odds, which averagely increased by 188%, 250%, 339%, and 369%, respectively.

CONCLUSIONS: WBMg was an independent risk factor of YOO, and the associations were stronger among those with unhealthy blood lipids. Our findings can help to guide clinical and public health policies on the relevance of magnesium nutritional status.

PMID:34481288 | DOI:10.1016/j.nut.2021.111425

Am J Emerg Med. 2021 Aug 24;50:404-408. doi: 10.1016/j.ajem.2021.08.057. Online ahead of print.

ABSTRACT

BACKGROUND: Poisonings resulting from the abuse of drugs currently represent a serious problem for public health. Among the main agents involved, cocaine stands out. It became one of the most abused drugs around the world, and one of the main reasons for visits to the emergency department due to the use of illicit substances. The use of cocaine is primarily in combination with alcoholic beverages. There are few studies that correlate cocaine blood concentration and the severity of clinical manifestations in patients evaluated at Emergency Department. The aim of the present study was to verify the possible relationship between the blood concentration of cocaine and cocaethylene (product of the interaction of cocaine with ethanol) with the severity of the clinical manifestations presented by patients with cocaine intoxication.

METHODS: Blood levels were measured by high-performance liquid chromatography (HPLC) and the severity of clinical manifestations was assessed using the Stimulant Intoxication Score (SIS). To establish this relationship, Pearson’s chi-square statistical test (x²) was used for categorical variables and Student’s t for continuous variables, with statistical significance of 5% (p < 0.05).

RESULTS: Of the 81 patients included in the study, 77.8% were men with a mean age of 32.5 years ± 8.5 and mean of SIS 3.4 ± 2.5. Considering the toxicological analysis results, 24.7% of the blood samples were positive. The mean of cocaine and cocaethylene concentrations were 0.34 μg/mL ± 0.45 and 0.38 μg/mL ± 0.34, respectively. The blood concentration of cocaine and cocaethylene has not been shown to be useful information for the treatment and prognosis of patients, but blood levels of these substances at the time of treatment, regardless of their concentration, may be an indicator of severity, showing that any concentrations of these substances should be considered as potentially toxic.

CONCLUSION: The application of the SIS score proved to be an important alternative capable of predicting the severity of the patients due to cocaine intoxication in a fast and simplified way.

PMID:34481259 | DOI:10.1016/j.ajem.2021.08.057

Public Health. 2021 Aug 31;198:196-199. doi: 10.1016/j.puhe.2021.07.021. Online ahead of print.

ABSTRACT

OBJECTIVES: Arts on Prescription (AoP) programmes were among the first forms of social prescribing in the UK. Most of the studies of AoP programmes focus on adults and currently there is no published research on the impact of AoP on children and young people. This study investigates the impact of 10 weekly AoP workshops delivered in a school setting on the mental well-being and resilience of adolescents aged 13-16 years at risk of emotional or behavioural problems.

STUDY DESIGN: The study design used is a longitudinal cohort study of an AoP programme implemented in 10 schools in the East of England.

METHODS: Changes in mental well-being and resilience of school children were assessed using the Warwick-Edinburgh Mental Well-being Scale (WEMWBS), and the True Resilience Scale applied pre- and post-intervention, with follow-up at 3 months. In total, 91 young people participated in the programme and 65 completed pre- and post-intervention measures.

RESULTS: Data from the WEMWBS and True Resilience Scale indicated that the AoP Programme had a positive impact on both well-being and resilience of participants with a statistically significant increase recorded immediately post-intervention. However, these improvements were not sustained upon observation at 3-month follow-up.

CONCLUSION: This article presents the first indication of the effectiveness of a programme of AoP workshops on the mental well-being and resilience of children and young people. It suggests the potential of AoP as a means of support the mental health and well-being of secondary school aged children.

PMID:34481274 | DOI:10.1016/j.puhe.2021.07.021

Comput Biol Med. 2021 Aug 28;137:104813. doi: 10.1016/j.compbiomed.2021.104813. Online ahead of print.

ABSTRACT

BACKGROUND: This study sought to evaluate the performance of machine learning (ML) models and establish an explainable ML model with good prediction of 3-year all-cause mortality in patients with heart failure (HF) caused by coronary heart disease (CHD).

METHODS: We established six ML models using follow-up data to predict 3-year all-cause mortality. Through comprehensive evaluation, the best performing model was used to predict and stratify patients. The log-rank test was used to assess the difference between Kaplan-Meier curves. The association between ML risk and 3-year all-cause mortality was also assessed using multivariable Cox regression. Finally, an explainable approach based on ML and the SHapley Additive exPlanations (SHAP) method was deployed to calculate 3-year all-cause mortality risk and to generate individual explanations of the model’s decisions.

RESULTS: The best performing extreme gradient boosting (XGBoost) model was selected to predict and stratify patients. Subjects with a higher ML score had a high hazard of suffering events (hazard ratio [HR]: 10.351; P < 0.001), and this relationship persisted with a multivariable analysis (adjusted HR: 5.343; P < 0.001). Age, N-terminal pro-B-type natriuretic peptide, occupation, New York Heart Association classification, and nitrate drug use were important factors for both genders.

CONCLUSIONS: The ML-based risk stratification tool was able to accurately assess and stratify the risk of 3-year all-cause mortality in patients with HF caused by CHD. ML combined with SHAP could provide an explicit explanation of individualized risk prediction and give physicians an intuitive understanding of the influence of key features in the model.

PMID:34481185 | DOI:10.1016/j.compbiomed.2021.104813

J Fluency Disord. 2021 Aug 28;70:105866. doi: 10.1016/j.jfludis.2021.105866. Online ahead of print.

ABSTRACT

The Communication Attitude Test for Adults who stutter (BigCAT) is an established measure of cognitive traits in adults who stutter (AWS). The primary purpose of the present study was to adapt and validate the BigCAT to the Kannada language. The secondary purpose was to compare AWS’ and adults who do not stutter (AWNS) BigCAT-K scores and compare AWS’ score in sub-populations in terms of severity and age. The study included a purposive sample of 100 AWS and 317 AWNS. There was high test-retest reliability and solid construct validity, as made evident by the results of the discriminant analysis and cross-validation. Further, as in other investigations with the BigCAT (Vanryckeghem & Brutten, 2019), this self-report test revealed a statistically significant group mean difference between AWS and AWNS, suggesting the presence of a negative attitude towards communication in Kannada-speaking AWS. Further, individuals with severe stuttering had a significantly higher level of speech-associated negative attitude compared to those with mild stuttering. Age does not seem to influence the AWS’ speech-associated belief system. Both of these findings augment the existing scant literature on exploring the association between stuttering severity and age on the cognitive dimension of stuttering. The outcomes establish the BigCAT-K as an effective tool in the assessment and subsequent management of stuttering.

PMID:34481196 | DOI:10.1016/j.jfludis.2021.105866

Cognition. 2021 Sep 1;217:104891. doi: 10.1016/j.cognition.2021.104891. Online ahead of print.

ABSTRACT

Does semantic information-in particular, regularities in category membership across objects-influence visual working memory (VWM) processing? We predict that the answer is “yes”. Four experiments evaluating this prediction are reported. Experimental stimuli were images of real-world objects arranged in either one or two spatial clusters. On coherent trials, all objects belonging to a cluster also belonged to the same category. On incoherent trials, at least one cluster contained objects from different categories. Experiments using a change-detection paradigm (Experiments 1-3) and an experiment in which participants recalled the locations of objects in a scene (Experiment 4) yielded the same result: participants showed better memory performance on coherent trials than on incoherent trials. Taken as a whole, these experiments provide the best (perhaps only) data to date demonstrating that statistical regularities in semantic category membership improve VWM performance. Because a conventional perspective in cognitive science regards VWM as being sensitive solely to bottom-up visual properties of objects (e.g., shape, color, orientation), our results indicate that cognitive science may need to modify its conceptualization of VWM so that it is closer to “conceptual short-term memory”, a short-term memory store representing current stimuli and their associated concepts (Potter, 1993, 2012).

PMID:34481197 | DOI:10.1016/j.cognition.2021.104891

Leuk Res. 2021 Aug 31;110:106701. doi: 10.1016/j.leukres.2021.106701. Online ahead of print.

ABSTRACT

AML with myelodysplasia-related changes (AML-MRC) is a subtype of AML known to have adverse prognosis. The karyotype abnormalities in AML-MRC have been well established; however, relatively little has been known about the role of gene mutation profiles by next generation sequencing. 177 AML patients (72 AML-MRC and 105 non-MRC AML) were analyzed by NGS panel covering 53 AML related genes. AML-MRC showed statistically significantly higher frequency of TP53 mutation, but lower frequencies of mutations in NPM1, FLT3-ITD^Low, FLT3-ITD^High, FLT3-TKD, NRAS, and PTPN11 than non-MRC AML. Supervised tree-based classification models including Decision tree, Random forest, and XGboost, and logistic regression were used to evaluate if the mutation profiles could be used to aid the diagnosis of AML-MRC. All methods showed good accuracy in differentiating AML-MRC from non-MRC AML with AUC (area under curve) of ROC ranging from 0.69 to 0.78. Additionally, logistic regression indicated 3 independent factors (age and mutations of TP53 and FLT3) could aid the diagnosis AML-MRC. Using weighted factors, a AML-MRC risk scoring equation was established for potential application in clinical setting: +1x(Age ≥ 65) + 3 x (TP53 mutation) – 2 x (FLT3 mutation). Using a cutoff score of 0, the accuracy of the risk score was 0.76 with sensitivity of 0.77 and specificity of 0.75 for predicting the diagnosis of AML-MRC. Further studies with larger sample sizes are warranted to further evaluate the potential of using gene mutation profiles to aid the diagnosis of AML-MRC.

PMID:34481124 | DOI:10.1016/j.leukres.2021.106701

Comput Biol Chem. 2021 Aug 19;94:107558. doi: 10.1016/j.compbiolchem.2021.107558. Online ahead of print.

ABSTRACT

Classifying proteins into their respective enzyme class is an interesting question for researchers for a variety of reasons. The open source Protein Data Bank (PDB) contains more than 1,60,000 structures, with more being added everyday. This paper proposes an attention-based bidirectional-LSTM model (ABLE) trained on over sampled data generated by SMOTE to analyse and classify a protein into one of the six enzyme classes or a negative class using only the primary structure of the protein described as a string by the FASTA sequence as an input. We achieve the highest F1-score of 0.834 using our proposed model on a dataset of proteins from the PDB. We baseline our model against eighteen other machine learning and deep learning networks, including CNN, LSTM, Bi-LSTM, GRU, and the state-of-the-art DeepEC model. We conduct experiments with two different oversampling techniques, SMOTE and ADASYN. To corroborate the obtained results, we perform extensive experimentation and statistical testing.

PMID:34481129 | DOI:10.1016/j.compbiolchem.2021.107558