Strong Predictor Research Articles

EXPRESS: Better face-name recall is associated with better face recognition ability.

Face recognition is a highly developed and specialized human ability, distinct from other cognitive abilities. Previous studies examining individual differences in face recognition have focused on face perception and specialized perceptual mechanisms such as holistic face processing. However, the contribution of specific face memory processes to face recognition ability remains unclear. In 99 neurotypical individuals, we administered validated face perception assessments, three face memory tasks (Old/New task, Face-Scene task, Face-Name/Occupation task), and the Cambridge Face Memory Test (CFMT) to assess face recognition ability. We found that after accounting for face perception ability (which significantly predicted face recognition ability), Face-Name recall and recollection of faces in the Face-Scene task predicted unique variance in face recognition ability, with Face-Name recall being the strongest predictor. This highlights that associative memory mechanisms contribute to face recognition abilities and suggest that the ability to learn and recall proper names is particularly important to face recognition.

The predictive utility of the in utero exposome for childhood adiposity in independent and integrated frameworks.

To assess the predictive potential of the in utero exposome in relation to childhood adiposity as indicated by body mass index z-scores (BMIz) and the fourth versus first quartile of % fat mass (FM) at median age of 4.6 years. We leveraged data on clinical risk factors for childhood obesity during the perinatal period, along with cord blood per/polyfluoroalkyl substances (PFAS) and cord blood DNA methylation, in 268 mother-offspring pairs. We used the sparsity ranked LASSO penalized regression framework for each outcome and assessed model performance based on % variability explained for BMIz and area under the receiver operating characteristic curve (AUC) for the fourth versus first quartile of %FM. We employed cross-validation for model tuning and split-sample validation for model evaluation. Mean ± SD BMIz was 0.01 ± 1.1, %FM was 19.8 ± 6.34%. The optimal model for predicting BMIz explained 19.1% of the variability in the validation set and included only clinical characteristics: maternal pre-pregnancy BMI, paternal BMI, gestational weight gain, physical activity during pregnancy and child race/ethnicity. The optimal model for fourth versus first quartiles of %FM achieved an AUC of 0.82 ± 0.01 in the validation set, with the clinical features again emerging as the strongest predictors. In this study sample, perinatal chemical exposures and the epigenome have low utility in predicting childhood adiposity, beyond known clinical risk factors.

Predicting UK Domestic Electricity and Gas Consumption between Differing Demographic Household Compositions

This paper examines the influence of building characteristics, occupant demographics and behaviour on gas and electricity consumption, differentiating between family groups; homes with children; homes with elderly; and homes without either. Both regression and Lasso regression analyses are used to analyse data from a 2019 UK-based survey of 4358homes (n = 1576 with children, n = 436 with elderly, n = 2330 without either). Three models (building, occupants, behaviour) were tested against electricity and gas consumption for each group. Results indicated that homes without children or elderly consumed the least energy. Property Type emerged as the strongest predictor in the Building Model (except for homes with elderly), while Current Energy Efficiency was less significant, particularly for homes with elderly occupants. Homeownership and number of occupants were the most influential factors in the Occupants Model, though this pattern did not hold for homes with elderly. Many occupant and behaviour variables are often considered ‘unregulated energy’ in calculations such as SAP and are thus typically disregarded. However, this study found these variables to be significant, especially as national standards improve. The findings suggest that incorporating occupant behaviour into energy modelling could help reduce the energy performance gap.

Predictive model for college students’ performance in higher mathematics

This study investigated the influence of psychological factors (attitude, learning engagement, motivation, and self-efficacy) and instructional factors (teacher-related factors and learning environment) on college students' performance in higher mathematics. It developed a predictive model for higher math performance. This utilized a cross-sectional research design using a survey to gather data at a university in Cebu province, Philippines, with 391 students from Bachelor of Science in Industrial Engineering and Bachelor of Secondary Education Major in Mathematics programs selected through purposive sampling, resulting in an 87% response rate. Data were analyzed using descriptive statistics, Pearson correlation, and multiple regression. Findings revealed that students generally held positive attitudes towards math, demonstrated average learning engagement, were highly motivated, and reported average self-efficacy. Both teacher-related factors and the learning environment were perceived very positively. While both psychological and instructional factors showed significant positive correlations with mathematics performance, multiple regression analysis indicated that only attitude was not a significant predictor. Learning engagement emerged as the strongest predictor, followed by learning environment and self-efficacy. The main contribution of this study is the creation of a predictive model, Higher Mathematics Performance = 1.82 + 0.008 (attitude) + 0.257 (learning engagement) + 0.039 (motivation) + 0.098 (self-efficacy) + 0.084 (teacher-related factors) + 0.155 (learning environment) + e, which accounted for 82.7% of the variance in higher mathematics performance. Hence, the predictive model developed in this study can serve as a valuable tool to design interventions and effective strategies for improving mathematics performance and overall academic achievement in higher education. This leads to a broad picture of the fact that these factors are correlated and how the various correlations can describe strategies for proposing relevant programs necessary for the improvement of performance in higher mathematics.

Assessing Produce Growers’ Perceptions and Adoption of Agricultural Water Safety Practices in the United States

The Produce Rule regulates food safety among produce growers in the United States. Policy exemptions (e.g., for farms with average produce sales under USD 30,000 over three years) pose a threat to local food safety, particularly given exempt growers’ role in fresh produce production. Understanding exempt growers’ perceptions of food safety practices and the factors influencing their voluntary adoption is crucial. A cross-sectional quantitative survey was conducted to assess growers’ current engagement, their perceptions of agricultural water safety practices, and their influence on adoption decisions. The findings indicate inadequate engagement with agricultural water safety practices, with production water-related safety practices showing higher engagement compared to post-harvest water practices. Overall, growers reported favorable perceptions toward agricultural water safety practices. The perceived compatibility, relative advantage, and observability predicted adoption decisions among exempt growers, with the perceived compatibility being the strongest predictor. Policymakers and education professionals should design strategies and tailored educational interventions and messaging for exempt growers to emphasize the compatibility, relative advantage, and observability of agricultural water safety practices. Furthermore, it is recommended to explore policies and other mechanisms to increase the perceived relative advantage (immediacy of rewards and direct benefits) for exempt growers to promote voluntary adoption of these on-farm food safety practices.

Human capital indicators as influenced by SES, cognitive and non-cognitive skills: evidence from Taiwan’s PISA performance

Taiwan’s high performance in the PISA surveys has not been unnoticed by researchers. However, both individual level research and cross-national research which is relevant for Taiwan’s PISA achievement is typically lacking, especially as regards later surveys. This article examined Taiwan’s PISA mathematics and reading achievement in 2018, using individual level data (Valid N = 6863) and cross-national data with aggregated data for 77 countries. At the individual level, socioeconomic and non-cognitive variables explain the most variance of PISA mathematics and reading. At the cross-national level, cognitive ability and average income were statistically significant with cognitive ability as the strongest predictor. As Taiwan has a high level of average cognitive ability it may regarded as a strong comparative advantage with regard to human capital.

Aortic Uptake of 18F-NaF and 18F-FDG and Calcification Predict the Development of Abdominal Aortic Aneurysms and Is Attenuated by Drug Therapy.

Abdominal aortic aneurysms expand over time and increase the risk of fatal ruptures. To predict expansion, the isolated assessment of 18F-fluorodeoxyglucose (FDG) and sodium fluoride (NaF) uptake or calcification volume in aneurysms has been investigated with variability in results. We systematically evaluated whether 18F-FDG and 18F-NaF uptake was predictive of abdominal aortic aneurysm expansion. Seventy-four male Sprague-Dawley rat abdominal aortic aneurysm models were imaged using positron emission tomography-computed tomography with 18F-FDG and 18F-NaF at 1, 2, 4, 6, and 8 weeks after CaCl2 or saline stimulation. In the 1-week cohort (n=25), the correlation between 18F-FDG or 18F-NaF uptake and pathological markers was investigated. In the time course cohort (n=49), animals received either atorvastatin, losartan, aldactone, or risedronate to assess the effect of these drugs, and the relationship between aortic size and sequential 18F-FDG and 18F-NaF uptake or calcification volume was examined. In the 1-week cohort, the maximum standard unit value of 18F-FDG and 18F-NaF uptake correlated with CD68- (r=0.82; P=0.001) and von Kossa staining-positive areas (r=0.89; P<0.001), respectively. In the time course cohort, 18F-FDG and 18F-NaF uptake changed in a time-dependent manner and drugs attenuated this uptake. Specifically, 18F-FDG showed high uptake at weeks 1 and 2, whereas a high 18F-NaF uptake was noted throughout the study period. Atorvastatin and risedronate showed a decreased and increased aortic size, respectively. The final aortic area correlated well with 18F-FDG and 18F-NaF uptake and calcification volume, especially at 1 and 2 weeks (18F-NaF [1 week]: r=0.61, 18F-FDG [2 weeks]: r=0.51, calcification volume [1 week]: r=0.59; P<0.001). Multiple linear regression analysis showed that the combination of these factors predicted the final aortic size, with 18F-NaF uptake at 1 week being the strongest predictor. The uptake of 18F-NaF and 18F-FDG and the calcification volume at appropriate times correlated with the development of abdominal aortic aneurysms, with 18F-NaF uptake being the strongest predictor.

Extending the theory of planned behavior to predict the behavior of farmers in choosing low-water-intensive medicinal plants

Water scarcity has become a serious challenge in many parts of the world due to increasing demands and the impacts of climate change. The agriculture sector globally accounts for a major portion of water consumption, yet it also holds substantial potential for water conservation. Among the most effective ways to conserve water is to cultivate low-water-demanding crops, such as medicinal plants (MPs), instead of water-demanding crops (WDC). However, the voluntary participation of farmers, largely influenced by socio-psychological drivers, is crucial for successfully implementing most water conservation programs and needs to be addressed. Therefore, the main objectives of this paper were: (1) to identify the determinants that explain farmers' intention and behavior in cultivating MPs instead of WDC; and (2) to examine the effectiveness and performance of an extended version of the theory of planned behavior (TPB) in predicting farmers' intention and behavior toward cultivating MPs by innovatively incorporating four new variables into the original TPB model: perceived barriers, moral norms, compatibility, and relative advantage. The applicability of the theoretical framework was evaluated in the Sojasroud Plain, Zanjan province, Iran. The results of the structural equation modeling revealed that: (1) farmers' intention to cultivate MPs instead of WDC is significantly influenced by perceived barriers, moral norms, subjective norms, and perceived behavior control (the strongest predictor); and (2) farmers’ behavior in cultivating MPs instead of WDC is predicted by relative advantage, compatibility, and intention (the most prominent determinant). The R2 values for predicting intention and behavior were 55% and 53%, respectively. Based on the results, some practical policies were proposed to increase the cultivation of MPs in the study area.

Dysphagia in primary progressive aphasia: Clinical predictors and neuroanatomical basis.

Dysphagia is an important feature of neurodegenerative diseases and potentially life-threatening in primary progressive aphasia (PPA) but remains poorly characterized in these syndromes. We hypothesized that dysphagia would be more prevalent in nonfluent/agrammatic variant (nfv)PPA than other PPA syndromes, predicted by accompanying motor features, and associated with atrophy affecting regions implicated in swallowing control. In a retrospective case-control study at our tertiary referral centre, we recruited 56 patients with PPA (21 nfvPPA, 22 semantic variant [sv]PPA, 13 logopenic variant [lv]PPA). Using a pro forma based on caregiver surveys and clinical records, we documented dysphagia (present/absent) and associated, potentially predictive clinical, cognitive, and behavioural features. These were used to train a machine learning model. Patients' brain magnetic resonance imaging scans were assessed using voxel-based morphometry and region-of-interest analyses comparing differential atrophy profiles associated with dysphagia presence/absence. Dysphagia was significantly more prevalent in nfvPPA (43% vs. 5% svPPA and no lvPPA). The machine learning model revealed a hierarchy of features predicting dysphagia in the nfvPPA group, with excellent classification accuracy (90.5%, 95% confidence interval = 77.9-100); the strongest predictor was orofacial apraxia, followed by older age, parkinsonism, more severe behavioural disturbance, and more severe cognitive impairment. Significant grey matter atrophy correlates of dysphagia in nfvPPA were identified in left middle frontal, right superior frontal, and right supramarginal gyri and right caudate. Dysphagia is a common feature of nfvPPA, linked to underlying corticosubcortical network dysfunction. Clinicians should anticipate this symptom particularly in the context of other motor features and more severe disease.

Systemic immune-inflammation index, and neutrophilto-lymphocyte and platelet-to-lymphocyte ratios can predict clinical outcomes in patients with acute coronary syndrome.

Inflammatory mechanisms play an important role in the pathogenesis of atherosclerosis and myocardial infarction. The clinical and prognostic importance of inflammatory parameters, such as neutrophil-lymphocyte (NLR) and platelet-lymphocyte ratios (PLR) in complete blood counts in acute myocardial infarction and other cardiovascular diseases has been demonstrated. However, systemic immune-inflammation index (SII) calculated from neutrophils, lymphocytes and platelets in the complete blood cell count has not been studied sufficiently and is thought to provide a better prediction. This study investigated whether haematological parameters such as SII, NLR and PLR were associated with clinical outcomes in acute coronary syndrome (ACS) patients. We included 1 103 patients who underwent coronary angiography for ACS between January 2017 and December 2021. The association between major adverse cardiac events (MACE) that developed in hospital and at 50 months of follow up and SII, NLR and PLR was compared. Long-term MACE were defined as mortality, re-infarction and target-vessel revascularisation. SII was calculated using the formula: NLR × total platelet count in the peripheral blood (per mm3). Of the 1 103 patients, 403 were diagnosed with ST-elevation myocardial infarction and 700 with non-STelevation myocardial infarction. The patients were divided into a MACE and a non-MACE group. In hospital and during the 50-month follow up, 195 MACE were observed. SII, PLR and NLR were found to be statistically significantly higher in the MACE group (p < 0.001). SII, C-reactive protein level, age and white blood cell count were independent predictors of MACE in ACS patients. SII was found to be a strong independent predictor of poor outcomes in ACS patients. This predictive power was greater than that of PLR and NLR.

Factors Predicting the Quality of Care Among Nurses in Tertiary Hospitals in Fiji: A Cross-sectional Study

Quality of care by nurses is a key factor in determining the success of healthcare delivery around the globe, which is impacted by a shortage of nurses, excessive workloads, and unfavorable working conditions, including in the Republic of Fiji Islands. Using the Quality Health Outcome Model, this descriptive-predictive, cross-sectional study examined the quality of care and its predictors among 744 Fijian registered nurses from three tertiary hospitals. Instruments for data collection were the Demographic Data Sheet, the Quality of Care Scale, the Participation in Decision Making Scale, the Relational Coordination Survey, the Perception of Organizational Change Scale, the Job Satisfaction Scale, and the Organizational Commitment Questionnaire. Descriptive statistics and logistic regression analysis were applied to analyze the data. This study’s findings are informative and offer a glimmer of hope since 72.58% of participants perceived the overall quality of care as good/excellent, indicating a positive baseline. Two factors, relational coordination and job satisfaction, significantly affected the perception of the quality of care. The study model explained 8.90% of the variance in quality of care, with relational coordination being the strongest predictor. These findings provide a clear path to improvement. A comprehensive model should be developed and tested to better understand the factors predicting Fiji’s quality of care before it can be used to design an effective intervention. Developing nursing skills, improving good communication and work environments, and providing high-quality education and training among nurses can significantly improve the quality of care. In addition, support from the government for appropriate medical equipment, recruitment and retention strategies for nurses, and promotion of standard of care from the Ministry of Health and Medical Services are recommended to enhance the quality of care, further bolstering this hopeful outlook. Further Fijian nursing research is clearly needed on this topic in the future.

Effects of Entrepreneurship Training, Digital Innovation, and Social Capital on Business Sustainability in the Creative Industry Sector

This study investigates the impact of Digital Innovation, Entrepreneurship Training, and Social Capital on Business Sustainability within the creative industries sector. Utilizing a quantitative research approach, data were collected from 150 business owners and managers using a structured questionnaire with a five-point Likert scale. The data were analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS 3) to test the proposed hypotheses. The results reveal that all three factors—Digital Innovation, Entrepreneurship Training, and Social Capital—have positive and significant effects on Business Sustainability. Specifically, Social Capital emerged as the strongest predictor, followed by Digital Innovation and Entrepreneurship Training. These findings underscore the importance of embracing digital technologies, investing in entrepreneurial education, and cultivating strong social networks to enhance the sustainability of creative enterprises. The study provides valuable insights for practitioners, policymakers, and educators in the creative industries, offering practical strategies for fostering long-term business success.

Effect of Access to Capital, Entrepreneurship Education, and HR Competencies on Start-up Success in the Technology Sector

This study examines the impact of access to capital, entrepreneurship education, and human resource (HR) competencies on the success of start-ups in the technology sector. Utilizing a quantitative approach, data were collected from 150 start-up firms through structured questionnaires using a Likert scale ranging from 1 to 5. The data were analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS 3). The results indicate that all three factors—access to capital, entrepreneurship education, and HR competencies—positively and significantly influence start-up success. Access to capital allows start-ups to invest in research and development, scale operations, and attract top talent, while entrepreneurship education equips entrepreneurs with essential skills for strategic decision-making. HR competencies, as the strongest predictor, underscore the critical role of a skilled workforce in driving innovation and sustaining growth in the technology sector. The findings provide valuable insights for entrepreneurs, educators, and policymakers seeking to support the development and success of technology start-ups.

Machine Learning Models for Predicting Mortality in Patients with Cirrhosis and Acute Upper Gastrointestinal Bleeding at an Emergency Department: A Retrospective Cohort Study.

Cirrhosis is a major global cause of mortality, and upper gastrointestinal (GI) bleeding significantly increases the mortality risk in these patients. Although scoring systems such as the Child-Pugh score and the Model for End-stage Liver Disease evaluate the severity of cirrhosis, none of these systems specifically target the risk of mortality in patients with upper GI bleeding. In this study, we constructed machine learning (ML) models for predicting mortality in patients with cirrhosis and upper GI bleeding, particularly in emergency settings, to achieve early intervention and improve outcomes. In this retrospective study, we analyzed the electronic health records of adult patients with cirrhosis who presented at an emergency department (ED) with GI bleeding between 2001 and 2019. Data were divided into training and testing sets at a ratio of 90:10. The ability of three ML models-a linear regression model, an XGBoost (XGB) model, and a three-layer neural network model-to predict mortality in the patients was evaluated. A total of 16,025 patients with cirrhosis and 32,826 ED visits for upper GI bleeding were included in the study. The in-hospital and ED mortality rates were 11.2% and 2.2%, respectively. The XGB model exhibited the highest performance in predicting both in-hospital and ED mortality (area under the receiver operating characteristic curve: 0.866 and 0.861, respectively). International normalized ratio, renal function, red blood cell distribution width, age, and white blood cell count were the strongest predictors in all the ML models. The median ED length of stay for the ED mortality group was 17.54 h (7.16-40.01 h). ML models can be used to predict mortality in patients with cirrhosis and upper GI bleeding. Of the three models, the XGB model exhibits the highest performance. Further research is required to determine the actual efficacy of our ML models in clinical settings.

Elemental and isotopic analysis of leaves predicts nitrogen-fixing phenotypes

Nitrogen (N)-fixing symbiosis is critical to terrestrial ecosystems, yet possession of this trait is known for few plant species. Broader presence of the symbiosis is often indirectly determined by phylogenetic relatedness to taxa investigated via manipulative experiments. This data gap may ultimately underestimate phylogenetic, spatial, and temporal variation in N-fixing symbiosis. Still needed are simpler field or collections-based approaches for inferring symbiotic status. N-fixing plants differ from non-N-fixing plants in elemental and isotopic composition, but previous investigations have not tested predictive accuracy using such proxies. Here we develop a regional field study and demonstrate a simple classification model for fixer status using nitrogen and carbon content measurements, and stable isotope ratios (δ15N and δ13C), from field-collected leaves. We used mixed models and classification approaches to demonstrate that N-fixing phenotypes can be used to predict symbiotic status; the best model required all predictors and was 80–94% accurate. Predictions were robust to environmental context variation, but we identified significant variation due to native vs. non-native (exotic) status and phylogenetic affinity. Surprisingly, N content—not δ15N—was the strongest predictor, suggesting that future efforts combine elemental and isotopic information. These results are valuable for understudied taxa and ecosystems, potentially allowing higher-throughput field-based N-fixer assessments.

Determining behavioral intention and its predictors toward preconception care use among reproductive age women in Arba Minch town, Southern Ethiopia, 2022: a cross-sectional study based on the theory of planned behavior

BackgroundPreconception care is a highly effective, evidence-based intervention aimed at promoting the health of reproductive-age women and reducing adverse pregnancy-related outcomes. The Ethiopian Ministry of Health plans to integrate preconception care services into the country’s existing healthcare system. However, women’s preferences may be influenced by their values and customs. Therefore, this study used the theory of planned behavior to assess women’s intention toward preconception care use and its predictors among reproductive-age women in Arba Minch town, southern Ethiopia.MethodsA community-based cross-sectional study was conducted in Arba Minch town from May 1 to 30, 2022. A simple random sampling technique was employed to select 415 study participants for data collection. Data were collected through a face-to-face interview using a pretested, structured questionnaire. Epi Data version 4.6 and SPSS version 26 were used for the entry and analysis of data, respectively. Multiple linear regression was performed to identify independent predictors of intention to use preconception care. The standardized β-coefficient was used as a measure of association. A P value of less than 0.05 was used to declare statistical significance.ResultsThis study had 415 participants, giving a response rate of 98.3%. The mean age of the participants was 28.4 (SD 5.18). The mean intention to use preconception care was 21.43 (SD 2.47). Direct perceived behavioral control (β = 0.263, p < 0.001), direct attitude (β = 0.201, p = 0.001), direct subjective norm (β = 0.158, p = 0.006), and age (β=-0.115, p = 0.023) were significant predictors of women’s behavioral intention to use preconception care.ConclusionThe study identified perceived behavioral control as the strongest predictor, followed by attitude and subjective norms, influencing the intention to use preconception care. These findings underscore the importance of integrating these predictors into health intervention programs aimed at promoting the implementation of preconception care services.

The relationship between social media dependency and psychological distress due to misunderstanding and fear of COVID-19 in medical students

Improper use of social media during the COVID-19 outbreak, leading to fear and misunderstanding, can contribute to psychological disorders in vulnerable populations. This descriptive and analytical cross-sectional study was conducted in 2023 on 511 medical students of Babol University of Medical Sciences. Data were collected using demographic, psychological distress, fear and misunderstanding questionnaires related to COVID-19 and social media dependency. A total of 511 medical students, with an average age of Mean and S.D; 23.57 ± 3.03 participated in the study. The average psychological distress score was 23.82 ± 7.73 (out of 54), the average score of social media dependency was 17.53 ± 3.09 (out of 30), for the fear of COVID-19 was12.63 ± 2.56 (out of 35), and for the misperception of COVID-19 was 0.53 ± 0.09 (out of 18). Path analysis results) showed that direct path from improper use of social media to psychological distress is significant (P < 0.001, B = 0.19) but this relationship is not significant through fear and misperception related to COVID-19. Improper use of social media, identified as the strongest predictor, can directly increase psychological distress in medical students, without mediation through fear and misperception related to COVID-19. These findings should be taken into consideration when designing and evaluating interventions aimed at promoting mental health and fostering appropriate use of social media among students during disease outbreaks.

Evaluating an Expanded Health Action Process Approach Model of Health Promotion to Predict Physical Activity Levels of Individuals With Chronic Health Conditions and Disabilities

Physical activity is associated with reduced stress, anxiety, and depression. This study evaluated autonomous motivation and social support for exercise in addition to the Health Action Process Approach (HAPA) model as predictors of physical activity among people with chronic health conditions and disabilities. This is secondary data analysis. Participants were 982 U.S. individuals with chronic health conditions and disabilities. Hierarchical regression analysis (HRA) was used to assess variable impact sequentially, and a serial multiple mediation analysis (SMMA) was utilized to test mediation effect. The HAPA model constructs, autonomous motivation, and social support for exercise accounted for 35% of the variance in physical activity scores, a large effect size. Commitment was the strongest predictor, followed by autonomous motivation and self-efficacy. SMMA results indicated that autonomous motivation was associated with self-efficacy and commitment, and these factors were significantly associated with physical activity. Self-efficacy and commitment significantly mediated the relationship between autonomous motivation and physical activity. Autonomous motivation thus plays an important role in predicting the physical activity levels of adults with disabilities, warranting the inclusion of an autonomy construct in the HAPA model.

EFL students’ writing achievement via the execution of scaffolding strategies instruction

This paper examined the effects of scaffolding strategies instruction on EFL students' writing achievement, and its consistency in writing sub-skills. The study followed a pretest-posttest quasi-experimental design, and data were collected from 48 students in each comparison and experimental group. Results from paired-samples t-test confirmed a significant writing achievement score difference between the pretest and posttest for the experimental group (p = .001), but an insignificant difference for the comparison group (p = .426). Independent-sample t-test of post-test yielded a significant difference between the two groups favoring the experimental class (p = .001). The findings verified scaffolding teaching's feasibility for improving students' writing. Results from MANOVA proved writing sub-skills content, vocabulary, grammar, mechanics and organization jointly account for significant variance between comparison and experimental groups writing. Separate results of the variables revealed composing relevant content as the stronger predictor (F (1, 94) = 35.08, p = .001), but no mechanics' use disparity between the two groups, F (1, 94) = .42, p = .520. The findings verified the scaffolding strategies' substantial role in enhancing learners' overall writing achievement and all writing sub-skills except for mechanics. Results from the questionnaire showed participants' positive perceptions about scaffolding strategies instruction. Based on the findings, we recommend scaffolding strategies teaching for EFL students' writing improvements.

Association between liver fibrosis scores and short-term clinical outcomes in hospitalized chronic kidney disease patients: a prospective observational study.

In resource-constrained countries, inadequate access to healthcare and prognostic tools can be the Achilles' heel in effectively managing chronic kidney disease (CKD). There is a significant similarity in the pathogenesis of CKD and liver fibrosis. The role of liver fibrosis (LF) scores in predicting short-term clinical outcomes in hospitalized patients with CKD is unknown. Our study aimed at calculating LF scores and studying the association of liver fibrosis with short-term mortality and morbidity in CKD patients. Patients aged above 15 years diagnosed with CKD as per the KDIGO criteria were enrolled. LF scores, namely, NFS, GPRI, and FIB-4 scores were calculated. Patients were followed up for a period of 28 days for good and poor composite outcomes, namely, the requirement of hemodialysis, non-invasive ventilation, prolonged hospital stay, and neurological and cardiovascular outcomes including death. Among 163 patients, 70.5% were below 60 years of age, 82.2% were male and 35% were diabetic. At 28-day follow up, 52.1% had poor composite outcome. The AUROC for GPRI and FIB-4 in predicting poor outcomes was 0.783 (95% CI: 0.71-0.855) (p < 0.001) and 0.62 (95% CI: 0.534-0.706) (p = 0.008), respectively. The AUROC for GPRI and NFS in predicting all-cause mortality was 0.735 (95% CI: 0.627-0.843) (p = 0.001) and 0.876 (95% CI, 0.8-0.952) (p < 0.001), respectively. We found a positive association between LF scores and CKD outcomes in hospitalized patients. The LF scores significantly predicted poor outcomes in patients with CKD. Among the scores, GPRI was found to be a stronger predictor in predicting outcomes in both diabetic and non-diabetic patients with CKD. A high GPRI score was also associated with poor outcomes and increased mortality in both diabetics and non-diabetics.