METHODS: A total 98 in-hospital first ever acute stroke patients were recruited, and their Barthel Index scores were measured at the time of discharge, at 1 month and 3 months post-discharge. The Barthel Index was scored through telephone interviews. We employed the random intercept model from linear mixed effect regression to model the change of Barthel Index scores during the three months intervals. The prognostic factors included in the model were acute stroke subtypes, age, sex and time of measurement (at discharge, at 1 month and at 3 month post-discharge).
RESULTS: The crude mean Barthel Index scores showed an increased trend. The crude mean Barthel Index at the time of discharge, at 1-month post-discharge and 3 months post-discharge were 35.1 (SD = 39.4), 64.4 (SD = 39.5) and 68.8 (SD = 38.9) respectively. Over the same period, the adjusted mean Barthel Index scores estimated from the linear mixed effect model increased from 39.6 to 66.9 to 73.2. The adjusted mean Barthel Index scores decreased as the age increased, and haemorrhagic stroke patients had lower adjusted mean Barthel Index scores compared to the ischaemic stroke patients.
CONCLUSION: Overall, the crude and adjusted mean Barthel Index scores increase from the time of discharge up to 3-month post-discharge among acute stroke patients. Time after discharge, age and stroke subtypes are the significant prognostic factors for Barthel Index score changes over the period of 3 months.
DESIGN: Retrospective observational study.
SETTING: A tertiary urogynaecological unit in Australia.
POPULATION: A total of 780 archived data sets of women seen for symptoms of lower urinary tract and pelvic floor dysfunction.
METHODS: Standardised in-house interview and assessment using the International Continence Society (ICS) pelvic organ prolapse quantification (POP-Q), and four-dimensional translabial ultrasound. Offline analysis for hiatal dimensions was undertaken blinded to history and clinical examination.
MAIN OUTCOME MEASURES: Hiatal area on maximum Valsalva.
RESULTS: Of 780 women, 64 were excluded because of missing ultrasound volumes, leaving 716 for analysis: 96% (n = 686) were parous, with a median parity of three (interquartile range, IQR 2-3), and 91.2% (n = 653) were vaginally parous. Levator avulsion was found in 21% (n = 148). The mean hiatal area on Valsalva was 29 cm(2) (SD 9.4 cm(2) ). On one-way anova, vaginal parity was significantly associated with hiatal area (P < 0.001). Most of the effect seems to occur with the first delivery. Subsequent deliveries do not seem to have any significant effect on hiatal dimensions. This remained true after controlling for potential confounding factors using multivariate regression analysis (P = 0.0123).
CONCLUSIONS: Vaginal parity was strongly associated with hiatal area on Valsalva. Most of this effect seems to be associated with the first vaginal delivery.
Objective: To determine the additional relationship between factors discovered by searching for sociodemographic and metastasis factors, as well as treatment outcomes, which could help improve the prediction of the survival rate in cancer patients. Material and Methods. A total of 56 patients were recruited from the ambulatory clinic at the Hospital Universiti Sains Malaysia (USM). In this retrospective study, advanced computational statistical modeling techniques were used to evaluate data descriptions of several variables such as treatment, age, and distant metastasis. The R-Studio software and syntax were used to implement and test the hazard ratio. The statistics for each sample were calculated using a combination model that included methods such as bootstrap and multiple linear regression (MLR).
Results: The statistical strategy showed R demonstrates that regression modeling outperforms an R-squared. It demonstrated that when data is partitioned into a training and testing dataset, the hybrid model technique performs better at predicting the outcome. The variable validation was determined using the well-established bootstrap-integrated MLR technique. In this case, three variables are considered: age, treatment, and distant metastases. It is important to note that three things affect the hazard ratio: age (β 1: -0.006423; p < 2e - 16), treatment (β 2: -0.355389; p < 2e - 16), and distant metastasis (β 3: -0.355389; p < 2e - 16). There is a 0.003469102 MSE for the linear model in this scenario.
Conclusion: In this study, a hybrid approach combining bootstrapping and multiple linear regression will be developed and extensively tested. The R syntax for this methodology was designed to ensure that the researcher completely understood the illustration. In this case, a hybrid model demonstrates how this critical conclusion enables us to better understand the utility and relative contribution of the hybrid method to the outcome. The statistical technique used in this study, R, demonstrates that regression modeling outperforms R-squared values of 0.9014 and 0.00882 for the predicted mean squared error, respectively. The conclusion of the study establishes the superiority of the hybrid model technique used in the study.
Methods: Cross-sectional data from 62 developing countries were used to run several multivariate linear regressions. R2 was used to compare the powers of MPI with income-poverties (income poverty gaps [IPG] at 1.9 and 3.1 USD) in explaining LE.
Results: Adjusting for controls, both MPI (β =-0.245, P<0.001) and IPG at 3.1 USD (β=-0.135, P=0.044) significantly correlates with LE, but not IPG at 1.9 USD (β=-0.147, P=0.135). MPI explains 12.1% of the variation in LE compared to only 3.2% explained by IPG at 3.1 USD. The effect of MPI on LE is higher on female (β=-0.210, P<0.001) than male (β=-0.177, P<0.001). The relative influence of the deprivation indictors on LE ranks as follows (most to least): Asset ownership, drinking water, cooking fuel, flooring, child school attendance, years of schooling, nutrition, mortality, improved sanitation, and electricity.
Conclusion: Interventions to reduce poverty and improve LE should be guided by MPI, not income poverty indices. Such policies should be female-oriented and prioritized based on the relative influence of the various poverty deprivation indicators on LE.
METHODS: Data for this study, consisting of 2202 older adults aged 60 years and above, were taken from a population-based survey entitled "Identifying Psychosocial and Identifying Economic Risk Factor of Cognitive Impairment among Elderly. Data analysis was conducted using the IBM SPSS Version 23.0.
RESULTS: The mean of MMSE was found to be 22.67 (SD = 4.93). The overall prevalence of selfreported diabetes was found to be 23.6% (CI95%: 21.8% - 25.4%). The result of independent t-test showed diabetic subjects had a higher mean score of MMSE (M = 23.05, SD =4 .55) than their counterparts without diabetes (M = 22.55, SD = 5.04) (t = -2.13 plinear regression analysis showed that diabetes was not significantly associated with cognitive function, after controlling the possible confounding factors.
CONCLUSIONS: The findings from the current study revealed that diabetes is not associated with cognitive decline. This study supports the findings that long-term treatment of diabetes may reduce the risk of cognitive decline. This finding may provide new opportunities for the prevention and management of cognitive decline.