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    <journal-meta />
    <article-meta>
      <title-group>
        <article-title>The Correlation Analysis of Clinic Data at Estimation of Motor Disturbances in Children with CP</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>O.V. Limanovskaya</string-name>
          <email>o.v.limanovskaia@urfu.ru</email>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>A.V. Pahtusov</string-name>
          <email>alexof3000@mail.ru</email>
          <xref ref-type="aff" rid="aff2">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>O.D. Davydov</string-name>
          <email>davod09@yandex.ru</email>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>D.G. Stepanenko</string-name>
          <email>stepanenko@bonum.info</email>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>A.S. Kleshev</string-name>
          <email>buzzondev@gmail.com</email>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>State Autonomous Health, Institution, Sverdlovsk Region Multidisciplic, Clinical, Medical Centre «Bonum»</institution>
          ,
          <addr-line>Ekaterinburg</addr-line>
          ,
          <country country="RU">Russia</country>
        </aff>
        <aff id="aff1">
          <label>1</label>
          <institution>UrFU</institution>
          ,
          <addr-line>Ekaterinburg</addr-line>
          ,
          <country country="RU">Russia</country>
        </aff>
        <aff id="aff2">
          <label>2</label>
          <institution>Ural Technical Institute of</institution>
          ,
          <addr-line>Communications and Informatics</addr-line>
          ,
          <institution>Branch Siberian State University, of Telecommunications and</institution>
          ,
          <addr-line>Informatics, Ekaterinburg</addr-line>
          ,
          <country country="RU">Russia</country>
        </aff>
      </contrib-group>
      <abstract>
        <p>This study aims to evaluate the relationship between motor functions in children with spastic cerebral palsy (CP). Fifty-three children with spastic CP participated in the study. The results of MRC, Modified Ashworth Scale (MAS), goniometry and Tardieu Scale were exanimate. The Pearson correlation coefficient was used to determinate the correlations. As a result, the data of every group tests are found to correlate with each other. These dependencies are visualized.</p>
      </abstract>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>-</title>
      <p>
        1 Introduction
The ICD 10 discriminate the cerebral palsy in children (CP) in the group of diseases of nervous system G80. Cerebral
palsy (CP) is a group of persistent (but not necessarily unchanged), movement, posture, muscle tone, and motor skills
disorders non-progressive, with early onset, due to non-progressive impairments, occurring on an immature brain or a
brain under development (prenatal, perinatal, postnatal during the first 3-4 years of life) [
        <xref ref-type="bibr" rid="ref1">1</xref>
        ]. However, the etiology of CP
is complex the main reason is brain disorders [
        <xref ref-type="bibr" rid="ref1">1</xref>
        ]. Therefore the nervous assistance has the main function in children with
CP rehabilitation. Despite the fact that these dependencies are determined and proved statistically for some clinical cases
[
        <xref ref-type="bibr" rid="ref2 ref3 ref4">2-4</xref>
        ], no attempts of its search in children with CP were undertaken.
      </p>
      <p>
        Statistical analysis is one of the foundations of evidence-based clinical practice; the tool for clarification interaction of
factors is its part the correlation analysis [
        <xref ref-type="bibr" rid="ref5">5</xref>
        ]. It is known as a widely used method in the analysis of clinic data in children
with CP [
        <xref ref-type="bibr" rid="ref6">6-8</xref>
        ]. The authors in [
        <xref ref-type="bibr" rid="ref6">6</xref>
        ] have exanimated the distribution of spasticity types in CP groups by means of
correlation analysis methods. In work [7], the correlation analysis helped to estimate the correlation between spasticity
and pain in adults and adolescents with cerebral palsy. In work [8], correlation analysis is used to found the correlation
between the therapeutic intensity of rehabilitation and functional improvement in children with cerebral palsy.
The aim of present work is to detect the correlations of clinic data at estimation of motor disturbances in children with CP
(force changes, tone in lower limbs, motion in hip joint, knee-joint, ankle joint).
      </p>
      <p>The data and methods
Since the data are numeric, we can apply the Pearson sample correlation coefficient or Spearman rank correlation
coefficient. Spearman rank correlation coefficient requires data with ranks. We have made attempt to rank data. After
ranking the data according to the three ranks – norm, middle and low, obtained volumes of rank data are very different
between each other. For instance, thanks to features characteristic in children in GMFCS II (MRC values limited by 4-5),
almost all MRC data in children in GMFCS II belong to one rank (norm), however, the spasticity data have wide space
with norm more than 140, middle – 140-130, low – lower 120. Therefore we consider applying the Pearson sample
correlation coefficient.</p>
      <p>All calculations have been performed in the framework Anaconda in Python.
2.1</p>
      <p>Results and discussions
After deletion of incomplete data, the sample volume is 42 patients. We consider the clinic data of children before the
first course of treatment.</p>
      <p>We have estimated Pearson sample correlation coefficients of all 66 factors with 20 target factors. To evaluate the
statistical significance of obtained correlation coefficients, we have applied the t-criteria calculated according to this
formula:
t
r  n 2</p>
      <p>(1),
1 r 2
where r – Pearson sample correlation coefficient, n – sample volume. Obtained values t criteria were compared with
critical value tcr which for sample volume n=42 and statistic importance α=0.05 is 2.021.</p>
      <p>As results, we have obtained the list of factors with the statistically significant influence on selected target factors. For
instance, the target factor “the force of adductors of right hip” has the strong statistically significant dependence on force
abductors right hip and force of extension muscle of left hip (see Table 3). The strong statistically significant dependence
is one with correlation coefficient more than 0.7.
Since the number of factor lists with statistically significant correlations is 20, there are too many tables with it, and it can
be weekly readable. Therefore we have presented these dependencies as the oriented graph (Figure 1 and 2). The graph
contains only strong dependences.</p>
    </sec>
    <sec id="sec-2">
      <title>Notes: L – left limb, R – right limb. The rest notations are explained in Table 1.</title>
    </sec>
    <sec id="sec-3">
      <title>Notes: L – left limb, R – right limb. The rest notations are explained in Table 1. As can be seen from figure 1, one does not detect statistically significant interference of force muscle and tone muscle, however, there are strong statistically significant dependences between goniometry of joint and hypertonia.</title>
      <p>Conclusion
The dependencies between muscle groups on their strength and tone were established by the methods of correlation
analysis of clinical data on motor impairment assessment. At the same time, there were no dependences between muscle
tone and strength. Dependencies were established between the amplitude of movements in the joints of the lower
extremities and the pathologically increased muscle tone. The obtained data are planned to be taken into account in
clinical practice when drawing up individual programs for the rehabilitation of children with cerebral palsy.
7. M. Flanigan, D. Gaebler-Spira, C. Marciniak, M. Kocherginsky. Correlation of spasticity and pain in adults and
adolescents with cerebral palsy. Development medicine &amp; Child neurology, 59(s3):100{Abstracts of 71st Annual
Meeting of the American Academy for Cerebral Palsy and Developmental Medicine (AACPDM), 2017.
8. S.Y. Kim, M.H. Moon, S.C. Huh, S.H. Ko, Y.B. Shin. Correlation between therapeutic intencity of rehabilitation and
functional improvement in children with celebral palsy. Annals of Physical and Rehabilitation medicine, 61:e310
{12th World Congress of the International Society of Physical and Rehabilitation Medicine, 2018.</p>
    </sec>
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