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A computerized system for the application of Basso, Beattie and Bresnahan scale in Wistar rats

Overview of attention for article published in Acta Ortopédica Brasileira, August 2015
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Title
A computerized system for the application of Basso, Beattie and Bresnahan scale in Wistar rats
Published in
Acta Ortopédica Brasileira, August 2015
DOI 10.1590/1413-78522015230400673
Pubmed ID
Authors

Alessandra Eira Iague Sleiman Molina, Alexandre Fogaça Cristante, Tarcisio Eloy Pessoa de Barros, Marcos Sleiman Molina, Tainá Peral Molina

Abstract

To develop and test a computer program to assist researchers in assigning scores in the application of the Basso, Beattie and Bresnahan (BBB) scale and to compare these scores when doing so in free, targeted and automated computer-assisted modes. To test the program, the participants used the Impactor methodology recommended by the New York University (USA), in which 12 Wistar rats submitted to spinal cord injury were filmed on the 28(th) day after the injury. Eight researchers from the Laboratory of Medical Investigation, Faculdade de Medicina da Universidade de São Paulo, SP, Brazil took part in the study. The two heads of the laboratory, with 15 years of experience in the application of the scale, were considered the gold standard. The results of the scale application were not significantly different in relation to the gold standard, considering the mean of the evaluators in each method: free, targeted and automated form (with the help of the computer). The application of the BBB scale in the automated mode, using the computer program, did not present any difference in relation to the gold standard for all the evaluators. Level of Evidence II, Diagnostic Studies.

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Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 27%
Student > Ph. D. Student 2 18%
Student > Master 2 18%
Professor > Associate Professor 1 9%
Student > Postgraduate 1 9%
Other 0 0%
Unknown 2 18%
Readers by discipline Count As %
Medicine and Dentistry 3 27%
Engineering 2 18%
Neuroscience 1 9%
Computer Science 1 9%
Unknown 4 36%