Early and accurate prediction of recovery is needed to assist treatment planning and inform patient selection in clinical trials. This study aimed to develop a prediction algorithm using a set of simple early clinical bedside measures to predict upper limb capacity at 3-months post-stroke. A secondary analysis of Stroke Arm Longitudinal Study at Gothenburg University (SALGOT) included 94 adults (mean age 68 years) with upper limb impairment admitted to stroke unit). Cluster analysis was used to define the endpoint outcome strata according to the 3-months Action Research Arm Test (ARAT) scores. Modelling was carried out in a training (70%) and testing set (30%) using traditional logistic regression, random forest models. The final algorithm included 3 simple bedside tests performed 3-days post stroke: ability to grasp, to produce any measurable grip strength and abduct/elevate shoulder. An 86–94% model sensitivity, specificity and accuracy was reached for differentiation between poor, limited and good outcome. Additional measurement of grip strength at 4 weeks post-stroke and haemorrhagic stroke explained the underestimated classifications. External validation of the model is recommended. Simple bedside assessments have advantages over more lengthy and complex assessments and could thereby be integrated into routine clinical practice to aid therapy decisions, guide patient selection in clinical trials and used in data registries.

The estimation of the parameters is one of an important issues in the mathematical statistics. The development of estimation methods requires accurate estimation and finds the best estimator for parameters. The aim of this research is to build a regression model. A dependent variable has a truncated t-distribution for this model. It was depended to the method of maximum likelihood for finding the parameters of the model, the parameters ? and ? 2 were estimated when they were unknown,  the approximation of the cumulative function of the truncated t-distribution from two sides  is used  to represent the function of the dependent variable. It was concluded that the value ? is equal ?ols plus a certain amount, this amount is equal zero when the values of the truncated points a, b are equal in value and different in sign.     As a practical application, simulation was used in data generation by using the program (Matlab 2020a), the value of ? was estimated, The comparison was between maximum likelihood method and according to the formula that we reached with its estimated value according to the least squares method based on the mean square errors, where its value according to the maximum likelihood method was less than its value according to the method of least squares, which indicates the advantage of the first method over the second.