Predicting recovery of cognitive function soon after stroke: differential modeling of logarithmic and linear regression.

Cognitive disorders in the acute stage of stroke are common and are important independent predictors of adverse outcome in the long term. Despite the impact of cognitive disorders on both patients and their families, it is still difficult to predict the extent or duration of cognitive impairments. The objective of the present study was, therefore, to provide data on predicting the recovery of cognitive function soon after stroke by differential modeling with logarithmic and linear regression. This study included two rounds of data collection comprising 57 stroke patients enrolled in the first round for the purpose of identifying the time course of cognitive recovery in the early-phase group data, and 43 stroke patients in the second round for the purpose of ensuring that the correlation of the early-phase group data applied to the prediction of each individual's degree of cognitive recovery. In the first round, Mini-Mental State Examination (MMSE) scores were assessed 3 times during hospitalization, and the scores were regressed on the logarithm and linear of time. In the second round, calculations of MMSE scores were made for the first two scoring times after admission to tailor the structures of logarithmic and linear regression formulae to fit an individual's degree of functional recovery. The time course of early-phase recovery for cognitive functions resembled both logarithmic and linear functions. However, MMSE scores sampled at two baseline points based on logarithmic regression modeling could estimate prediction of cognitive recovery more accurately than could linear regression modeling (logarithmic modeling, R(2) = 0.676, P<0.0001; linear regression modeling, R(2) = 0.598, P<0.0001). Logarithmic modeling based on MMSE scores could accurately predict the recovery of cognitive function soon after the occurrence of stroke. This logarithmic modeling with mathematical procedures is simple enough to be adopted in daily clinical practice

Multidimensional Psychometrics of Teacher Educators’ Professional Identity: An Initial Validation with Teacher Educators in Southeast Asia

While prior quantitative research has proposed a scale for measuring teacher educators’ professional identity in terms of two dimensions, a qualitative study and comprehensive review of the literature suggested that this factor actually consists of six dimensions: teacher of teachers, researcher, coach, curriculum developer, gatekeeper, and broker. The purpose of this study was to examine whether it is possible to measure teacher educators’ professional identity in terms of these six dimensions. A total of 192 teacher educators from Southeast Asian countries (Cambodia, Vietnam, and Thailand) participated in this study and responded to items we developed to measure their professional identity in addition to providing their basic attributes such as age, gender, highest level of education, and years of service as teachers or teacher educators. The results of a confirmatory factor analysis indicated that the six-factor model was acceptable and permitted the assessment of teacher educators’ professional identity in terms of six dimensions. The results also show that women exhibit a stronger professional identity as teachers of teachers than do men and that the higher the respondents’ highest level of education, the stronger their professional identity as teachers of teachers, coaches, curriculum developers, or gatekeepers

Profile of Recovery on Mini-Mental State Examination.

<p>Values are mean ± SD or median (interquartile range).</p><p>MMSE: Mini-Mental State Examination.</p>*<p><i>P</i><0.0001 for difference between actual and predicted values (linear regression analysis).</p>†<p><i>P</i><0.0001 for difference between logarithmic and linear regression model (χ2 test).</p

The Relationship between Walking Speed and Step Length in Older Aged Patients

Compared with elderly people who have not experienced falls, those who have were reported to have a shortened step length, large fluctuations in their pace, and a slow walking speed. The purpose of this study was to elucidate the step length required to maintain a walking speed of 1.0 m/s in patients aged 75 years or older. We measured the 10 m maximum walking speed in patients aged 75 years or older and divided them into the following two groups: Those who could walk 1.0 m/s or faster (fast group) and those who could not (slow group). Step length was determined from the number of steps taken during the 10 m-maximum walking speed test, and the step length-to-height ratio was calculated. Isometric knee extension muscle force (kgf), modified functional reach (cm), and one-leg standing time (s) were also measured. We included 261 patients (average age: 82.1 years, 50.6% men) in this study. The fast group included 119 participants, and the slow group included 142 participants. In a regression logistic analysis, knee extension muscle force (p = 0.03) and step length-to-height ratio (p &lt; 0.01) were determined as factors significantly related to the fast group. As a result of ROC curve analysis, a step length-to-height ratio of 31.0% could discriminate between the two walking speed groups. The results suggest that the step length-to-height ratio required to maintain a walking speed of 1.0 m/s is 31.0% in patients aged 75 years or older

Logarithmic modeling and linear regression modeling.

<p>A generic structure of logarithmic (A) and linear regression (B) modeling is given in a simple formula (independent variable = days from onset). MMSE: Mini-Mental State Examination; Ln: natural logarithm. ΔMMSE indicates change in MMSE scores between Day A and Day B. X can be calculated with this formula.</p

Scatterplots showing the relations between MMSE scores actually obtained and predicted MMSE scores.

<p>Predicted and actual MMSE scores at the third (open symbols) and fourth (filled symbols) sets of assessment by logarithmic model (A) and linear regression model (B). Logarithmic regression modeling estimated prediction of cognitive recovery to a more accurate degree than did the linear approach (logarithmic modeling, third set of assessments: R² = 0.676, <i>P</i><0.0001, fourth set of assessments: R² = 0.521, <i>P</i><0.0001; linear regression modeling, third set of assessments: R² = 0.598, <i>P</i><0.0001, fourth set of assessments: R² = 0.370, <i>P</i><0.0001).</p

Baseline Characteristics of the Study Group.

<p>Values are mean ± SD, n, or median (interquartile range).</p><p>PACI, partial anterior circulation infarct; POCI, posterior circulation infarct; LACI, lacunar infarcts.</p