BACKGROUND: Persons with dementia in nursing homes often experience cognitive decline (e.g., memory and visuospatial/construction problems), behavioural and psychological symptoms of dementia (BPSD), and impaired activities of daily living. Therefore, comprehensive interventions for this population are needed. We assessed whether a multimodal non-pharmacological intervention improved cognitive functions, BPSD, and activities of daily living in persons with dementia in nursing homes.
METHODS: This was a multicentre randomised controlled trial. Participants were 40 persons with dementia (38 women) living in four nursing homes (intervention group = 21; control group = 19). The intervention was conducted for 8 weeks, followed by an 8-week follow-up. Outcome measures were scores on the Japanese version of the Neurobehavioral Cognitive Status Examination Five and the ABC Dementia Scale. In the analysis, parameters were estimated using Bayesian statistics and a mixed-effects linear regression model for the change in each outcome measure.
RESULTS: There were significant between-group differences in changes in ABC Dementia Scale BPSD scores (8-week: posteriori median = 1.66, 95% Bayesian confidence interval 0.36-3.01; 16-week: median = 2.37, 95% Bayesian confidence interval 0.05-4.65). There was also a significant between-group difference in changes in Neurobehavioral Cognitive Status Examination Five constructional ability scores (16-week: median = 0.93, 95% Bayesian confidence interval 0.35-1.50).
CONCLUSIONS: This intervention may have a maintenance and improvement effect on BPSD in persons with dementia in nursing homes, and a sustained effect on constructional ability post-intervention. The intervention may be useful and easy to apply in practice.

BACKGROUND: Frailty is a multifactorial syndrome; through this study, we aimed to investigate the physiological, psychological, and social factors associated with frailty and frailty worsening in community-dwelling older adults.
METHODS: We conducted a cross-sectional and longitudinal study using data from the \"Community Empowerment and Well-Being and Healthy Long-term Care: Evidence from a Cohort Study (CEC),\" which focuses on community dwellers aged 65 and above in Japan. The sample of the cross-sectional study was drawn from a CEC study conducted in 2014 with a total of 673 participants. After excluding those who were frail during the baseline assessment (2014) and at the 3-year follow-up (2017), the study included 373 participants. Frailty assessment was extracted from the Kihon Checklist, while social relationships were assessed using the Social Interaction Index (ISI). Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and their predictive abilities were tested. Factors associated with frailty status and worsening were identified through the Maximum-min Hillclimb algorithm applied to Bayesian networks (BNs).
RESULTS: At baseline, 14.1% (95 out of 673) participants were frail, and 24.1% (90 out of 373) participants experienced frailty worsening at the 3-years follow up. LASSO regression identified key variables for frailty. For frailty identification (cross-sectional), the LASSO model\'s AUC was 0.943 (95%CI 0.913-0.974), indicating good discrimination, with Hosmer-Lemeshow (H-L) test p = 0.395. For frailty worsening (longitudinal), the LASSO model\'s AUC was 0.722 (95%CI 0.656-0.788), indicating moderate discrimination, with H-L test p = 0.26. The BNs found that age, multimorbidity, function status, and social relationships were parent nodes directly related to frailty. It revealed an 85% probability of frailty in individuals aged 75 or older with physical dysfunction, polypharmacy, and low ISI scores; however, if their social relationships and polypharmacy status improve, the probability reduces to 50.0%. In the longitudinal-level frailty worsening model, a 75% probability of frailty worsening in individuals aged 75 or older with declined physical function and ISI scores was noted; however, if physical function and ISI improve, the probability decreases to 25.0%.
CONCLUSIONS: Frailty and its progression are prevalent among community-dwelling older adults and are influenced by various factors, including age, physical function, and social relationships. BNs facilitate the identification of interrelationships among these variables, quantify the influence of key factors. However, further research is required to validate the proposed model.

BACKGROUND: In the United States, leading medical societies recommend 81 mg of aspirin daily for the prevention of preeclampsia (PE) in women at risk, whereas the NICE guidelines in the UK recommend a dose as high as 150 mg of aspirin. Recent data also suggest that in the obese population, inadequate dosing or aspirin resistance may impact the efficacy of aspirin at the currently recommend doses.
OBJECTIVE: We evaluated whether daily administration of 162 mg aspirin would be more effective compared to 81 mg in decreasing the rate of PE with severe features in high-risk obese pregnant individuals.
METHODS: We performed a randomized trial between May 2019 and November 2022. Individuals at 12 to 20-weeks gestational age (GA) with a BMI ≥ 30 kg/m2 at time to enrollment, and at least one of three high risk factors: history of PE in a prior pregnancy, at least stage I hypertension documented in the index pregnancy, pre-gestational diabetes or gestational diabetes diagnosed prior to 20 weeks GA were randomized to either 162 mg or 81 mg of aspirin daily till delivery, participants were not blinded to treatment allocation. Exclusion criteria were: multifetal gestation, known major fetal anomalies, seizure disorder, baseline proteinuria, on aspirin due to other indications, or contraindication to aspirin. The primary outcome was PE with severe features (PE or superimposed PE with severe features, eclampsia, or HELLP). Secondary outcomes included rates of preterm birth due to PE, small for gestational age (SGA), postpartum hemorrhage, abruption, and medication side effects. A sample size of 220 was needed using a preplanned Bayesian analysis of the primary outcome to estimate the posterior probability of benefit or harm with a neutral informative prior.
RESULTS: Of 343 eligible individuals, 220 (64.1%) were randomized. The primary outcome was available for 209/220 (95%). Baseline characteristics were similar between groups, median gestational age at enrollment was 15.9 weeks in the 162 mg aspirin group and 15.6 weeks in the 81 mg aspirin group. Enrollment prior to 16 weeks occurred in 55/110 of those assigned to 162 mg and 58/110 of those assigned to 81 mg of aspirin. The primary outcome occurred in 35% in the 162 mg aspirin group and in 40% in the 81 mg aspirin group (posterior relative risk, 0.88; 95% credible interval, 0.64-1.22). Bayesian analysis indicated a 78% probability of a reduction in the primary outcome with 162 mg aspirin compared to 81 mg aspirin dose. Rates of indicated preterm birth due to preeclampsia (21% vs 21%), SGA (6.5% vs 2.9%), abruption (2.8% vs 3.0%) and postpartum hemorrhage (10% vs 8.8%) were similar between groups. Medication adverse effects were also similar.
CONCLUSIONS: Among high-risk obese individuals, there was 78% probability of benefit that 162 mg aspirin compared to 81 mg will decrease the rate of PE with severe features. With a best estimate of a 12% reduction when using 162 mg of aspirin in comparison to 81 mg of aspirin in this population. This trial supports doing a larger multicenter trial.

UNASSIGNED: Severe acute malnutrition remains a significant health challenge, particularly in low- and middle-income countries. The aim of this study was to determine the survival time of under-five children with severe acute malnutrition.
UNASSIGNED: A retrospective cohort study was conducted at a hospital, focusing on under-five children with severe acute malnutrition. The study included 322 inpatients admitted to the Chiro hospital in Chiro, Ethiopia, between September 2019 and August 2020, whose data was obtained from medical records. Survival functions were analysed using Kaplan‒Meier plots and log-rank tests. The survival time of severe acute malnutrition was further analysed using the Cox proportional hazards model and Bayesian parametric survival models, employing integrated nested Laplace approximation methods.
UNASSIGNED: Among the 322 patients, 118 (36.6%) died as a result of severe acute malnutrition. The estimated median survival time for inpatients was found to be 2 weeks. Model selection criteria favored the Bayesian Weibull accelerated failure time model, which demonstrated that age, body temperature, pulse rate, nasogastric (NG) tube usage, hypoglycemia, anemia, diarrhea, dehydration, malaria, and pneumonia significantly influenced the survival time of severe acute malnutrition.
UNASSIGNED: This study revealed that children below 24 months, those with altered body temperature and pulse rate, NG tube usage, hypoglycemia, and comorbidities such as anemia, diarrhea, dehydration, malaria, and pneumonia had a shorter survival time when affected by severe acute malnutrition under the age of five. To reduce the death rate of children under 5 years of age, it is necessary to design community management for acute malnutrition to ensure early detection and improve access to and coverage for children who are malnourished.

UNASSIGNED: Glioblastoma (GBM) is a highly malignant brain tumor, and immune cells play a crucial role in its initiation and progression. The immune system\'s cellular components, including various types of lymphocytes, macrophages, and dendritic cells, among others, engage in intricate interactions with GBM. However, the precise nature of these interactions remains to be conclusively determined.
UNASSIGNED: In this study, a comprehensive two-sample Mendelian Randomization (MR) analysis was conducted to elucidate the causal relationship between immune cell features and the incidence of GBM. Utilizing publicly available genetic data, we investigated the causal associations between 731 immune cell signatures and the risk of GBM. Subsequently, we conducted a reverse Mendelian randomization analysis to rule out reverse causation. Finally, it was concluded that there is a unidirectional causal relationship between three subtypes of immune cells and GBM. Comprehensive sensitivity analyses were employed to validate the results robustness, heterogeneity, and presence of horizontal pleiotropy. To enhance the accuracy of our results, we concurrently subjected them to Bayesian analysis.
UNASSIGNED: After conducting MR analyses, we identified 10 immune phenotypes that counteract glioblastoma, with the most protective being FSC-A on Natural Killer T cells (OR = 0.688, CI = 0.515-0.918, P = 0.011). Additionally, we found 11 immune cell subtypes that promote GBM incidence, including CD62L- HLA DR++ monocyte % monocyte (OR = 1.522, CI = 1.004-2.307, P = 0.048), CD4+CD8+ T cell % leukocyte (OR = 1.387, CI = 1.031-1.866, P = 0.031). Following the implementation of reverse MR analysis, where glioblastoma served as the exposure variable and the outcomes included 21 target immune cell subtypes, we discerned that only three cell subtypes (CD45 on CD33+ HLA DR+ CD14dim, CD33+ HLA DR+ Absolute Count, and IgD+ CD24+ B cell Absolute Count) exhibited a unidirectional causal association with glioblastoma.
UNASSIGNED: Our study has genetically demonstrated the close relationship between immune cells and GBM, guiding future clinical research.

BACKGROUND: Residents in nursing homes are prone to cognitive decline affecting memory, visuospatial cognition, and executive functions. Cognitive decline can lead to dementia, necessitating prioritized intervention.
METHODS: The current study aimed to investigate whether an intervention using a digital game was effective for preserving and improving the cognitive function of residents in nursing homes. An intervention study was conducted using a single-case AB design with multiple baselines. The participants in the study were five older adults aged 65 and over who do not play digital games regularly. The study ran for 15 weeks, including a baseline (phase A) and an intervention phase (phase B). Phase A had five baselines (5 to 9 weeks) with random participant assignment. In phase B, participants engaged in a digital game (Space Invaders) individually. Cognitive function was assessed as the outcome, measured using the Brain Assessment (performed on a tablet through the Internet) at 16 measurement points. Four of five participants (two female and two male) were included in the analysis, using visual inspection and Bayesian statistics with multi-level modeling.
RESULTS: Visual inspection of the graphs revealed cognitive function score improvements after the intervention for most layers in terms of memory of numbers, memory of words, mental rotation test (visuospatial ability), and total scores in the Brain Assessment. These effects were also significant in the analysis by multi-level modeling.
CONCLUSIONS: The results suggest that the use of digital games may be effective for preserving and improving cognitive function among residents of nursing home.
BACKGROUND: This study was registered in the University Hospital Medical Information Network Clinical Trials Registry (UMIN000048677; public title: Effect of a Digital Game Intervention for Cognitive Functions in Older People; registration date: August 30, 2022).

Analytic methods development, like many other disciplines, relies on experimentation and data analysis. Determining the contribution of a paper or report on a study incorporating data analysis is typically left to the reviewer\'s experience and good sense, without reliance on structured guidelines. This is amplified by the growing role of machine learning driven analysis, where results are based on computer intensive algorithm applications. The evaluation of a predictive model where cross validation was used to fit its parameters adds challenges to the evaluation of regression models, where the estimates can be easily reproduced. This lack of structure to support reviews increases uncertainty and variability in reviews. In this paper, aspects of statistical assessment are considered. We provide checklists for reviewers of applied statistics work with a focus on analytic method development. The checklist covers six aspects relevant to a review of statistical analysis, namely: (1) study design, (2) algorithmic and inferential methods in frequentism analysis, (3) Bayesian methods in Bayesian analysis (if relevant), (4) selective inference aspects, (5) severe testing properties and (6) presentation of findings. We provide a brief overview of these elements providing references for a more elaborate treatment. The robustness analysis of an analytical method is used to illustrate how an improvement can be achieved in response to questions in the checklist. The paper is aimed at both engineers and seasoned researchers.

BACKGROUND: Standardization of procedures improves outcomes. Though systematic reviews have summarized the evidence-based steps (EBS) of cesarean delivery (CD), their bundled implementation has not been investigated.
OBJECTIVE: In this pre- and post-implementation trial, we sought to ascertain if bundled EBS of CD, compared to surgeon\'s preference, improves outcomes.
METHODS: A StaRI (Standards for Reporting Implementation Studies) compliant, multi-center pre- and post-implementation trial at 4 teaching hospitals was conducted. The pre-implementation period consisted of CD done based on the physicians\' preferences for 3 months; educational intervention (e.g., didactics, badge cards, posters, video) occurred at the 4th month. CDs in post-implementation period employed the bundled EBS. A pre-planned 10% randomized audit of both groups assessed adherence and uptake of EBS. The primary outcome was a composite maternal morbidity (CMM), which included estimated blood loss > 1,000 mL, blood transfusion, endometritis, post-partum fever, wound complications, sepsis, thrombosis, ICU admission, hysterectomy, or death. The secondary outcome was a composite neonatal morbidity (CNM) and some of its components were 5-min Apgar score < 7, positive pressure oxygen use, hypoglycemia, or sepsis. A priori Bayesian sample size calculation indicated 700 CD in each group was needed to demonstrate 20% relative reduction (from 15% to 12%) of CMM with 75% certainty. Bayesian logistic regression with neutral priors was used to calculate likelihood of net-improvement in adjusted relative risk (aRR) with 95% credible intervals (CrI).
RESULTS: A total of 1,425 consecutive CD (721 in pre- and 704 in post-implementation group) were examined. Audited data indicated that the baseline EBS utilization rate during the pre-implementation period was 79%; after the implementation bundled EBS of CD the audited adherence was 89%-an uptake of 10.0% of the EBS. In four aspects, the maternal characteristics differed significantly in the pre- and post-implementation periods: race/ethnicity, hypertensive disorder, and the relative contribution of the 4 centers to the cohorts and the gestational age at delivery, but the indications for CD and whether its duration was < versus > 60 min did not. The rates of CMM in the pre- and post-implementation groups were 26% and 22%, respectively (aRR, 0.88; 95% CrI, 0.73-1.04), with a 94 % Bayesian probability of a reduction in CMM. The CNM occurred in 37% of the pre- and in 41% of the post-implementation group (aRR, 1.12; 95% CrI 0.98-1.39), with a 95% Bayesian probability of worsening in CNM. When CMM were segregated by preterm (<37 wks) and term (> 37 weeks) CD, the improvement in maternal outcomes persisted; when CNM were segregated by gestational age subgroupsthe potential for worsening neonatal outcomes persisted as well.
CONCLUSIONS: Standardization of the evidence-based bundled steps of cesarean delivery resulted in a modest reduction of the composite maternal outcome; however, a paradoxical increase in neonatal composite morbidity was noted. Although individual evidence-based steps may be of value, while awaiting additional intervention trials a formal bundling of such steps is currently not recommended.

In low-risk pregnancies, a third-trimester ultrasound examination is indicated if fundal height measurement and gestational age discrepancy are observed. Despite potential improvement in the detection of ultrasound abnormality, prior trials to date on universal third-trimester ultrasound examination in low-risk pregnancies, compared with indicated ultrasound examination, have not demonstrated improvement in neonatal or maternal adverse outcomes.
The primary objective was to determine if universal third-trimester ultrasound examination in low-risk pregnancies could attenuate composite neonatal adverse outcomes. The secondary objectives were to compare changes in composite maternal adverse outcomes and detection of abnormalities of fetal growth (fetal growth restriction or large for gestational age) or amniotic fluid (oligohydramnios or polyhydramnios).
Our pre-post intervention study at 9 locations included low-risk pregnancies, those without indication for ultrasound examination in the third trimester. Compared with indicated ultrasound in the preimplementation period, in the postimplementation period, all patients were scheduled for ultrasound examination at 36.0-37.6 weeks. In both periods, clinicians intervened on the basis of abnormalities identified. Composite neonatal adverse outcomes included any of: Apgar score ≤5 at 5 minutes, cord pH <7.00, birth trauma (bone fracture or brachial plexus palsy), intubation for >24 hours, hypoxic-ischemic encephalopathy, seizure, sepsis (bacteremia proven with blood culture), meconium aspiration syndrome, intraventricular hemorrhage grade III or IV, periventricular leukomalacia, necrotizing enterocolitis, stillbirth after 36 weeks, or neonatal death within 28 days of birth. Composite maternal adverse outcomes included any of the following: chorioamnionitis, wound infection, estimated blood loss >1000 mL, blood transfusion, deep venous thrombus or pulmonary embolism, admission to intensive care unit, or death. Using Bayesian statistics, we calculated a sample size of 600 individuals in each arm to detect >75% probability of any reduction in primary outcome (80% power; 50% hypothesized risk reduction).
During the preintervention phase, 747 individuals were identified during the initial ultrasound examination, and among them, 568 (76.0%) met the inclusion criteria at 36.0-37.6 weeks; during the postintervention period, the corresponding numbers were 770 and 661 (85.8%). The rate of identified abnormalities of fetal growth or amniotic fluid increased from between the pre-post intervention period (7.1% vs 22.2%; P<.0001; number needed to diagnose, 7; 95% confidence interval, 5-9). The primary outcome occurred in 15 of 568 (2.6%) individuals in the preintervention and 12 of 661 (1.8%) in the postintervention group (83% probability of risk reduction; posterior relative risk, 0.69 [95% credible interval, 0.34-1.42]). The composite maternal adverse outcomes occurred in 8.6% in the preintervention and 6.5% in the postintervention group (90% probability of risk; posterior relative risk, 0.74 [95% credible interval, 0.49-1.15]). The number needed to treat to reduce composite neonatal adverse outcomes was 121 (95% confidence interval, 40-200). In addition, the number to reduce composite maternal adverse outcomes was 46 (95% confidence interval, 19-74), whereas the number to prevent cesarean delivery was 18 (95% confidence interval, 9-31).
Among low-risk pregnancies, compared with routine care with indicated ultrasound examination, implementation of a universal third-trimester ultrasound examination at 36.0-37.6 weeks attenuated composite neonatal and maternal adverse outcomes.

The generalized estimating equations method (GEE) is commonly applied to analyze data obtained from family studies. GEE is well known for its robustness on misspecification of correlation structure. However, the unbalanced distribution of family sizes and complicated genetic relatedness structure within each family may challenge GEE performance. We focused our research on binary outcomes. To evaluate the performance of GEE, we conducted a series of simulations, on data generated adopting the kinship matrix (correlation structure within each family) from the Strong Heart Family Study (SHFS). We performed a fivefold cross-validation to further evaluate the GEE predictive power on data from the SHFS. A Bayesian modeling approach, with direct integration of the kinship matrix, was also included to contrast with GEE. Our simulation studies revealed that GEE performs well on a binary outcome from families having a relatively simple kinship structure. However, data with a binary outcome generated from families with complex kinship structures, especially with a large genetic variance, can challenge the performance of GEE.