Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Monday, April 11, 2016

A prediction model for activities of daily living for stroke patients in a convalescent rehabilitation ward

Rather than useless crap research like this our stroke leaders should be focusing on SOLVING  the problems in stroke. Not just predicting the problems.
https://www.jstage.jst.go.jp/article/jalliedhealthsci/7/1/7_1/_article



PURPOSE: To create a model to predict independence in the activities of daily living at discharge in stroke patients in the convalescence stage. The study also examined whether the predictability of functional independence at discharge would be improved by creating a specific prediction model for each rehabilitation facility. METHODS: To create the prediction model, data of 65 first stroke patients were analyzed using stepwise multiple regression analysis. Age, time post-stroke, Functional Independence Measure motor subscale score, Functional Independence Measure cognitive subscale score, Stroke Impairment Assessment Set, Berg Balance Scale, and Vitality Index at admission were selected as predictor variables of Functional Independence Measure motor subscale score at discharge. The accuracy of this model was compared with an existing prognosis model using data from 98 first-stroke patients, comparing the difference between actual and predicted Functional Independence Measure motor subscale score at discharge for each model. RESULTS: The prediction formula created included admission Functional Independence Measure motor subscale score, Vitality Index, age, and Stroke Impairment Assessment Set score. The adjusted R square value was 0.60. The prediction errors of the new and previous models were −2.5 ± 10.8 and −18.3 ± 18.7, respectively, which were significantly different. CONCLUSION: Our results suggest that prediction accuracy may be improved by creating prediction formulas specifically for each institution.

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