I see zero use for assessments, they directly do nothing for recovery. Why not deliver EXACT STROKE PROTOCOLS THAT DELIVER 100% RECOVERY, instead of this lazy shit.
Oops, I'm not playing by the polite rules of Dale Carnegie, 'How to Win Friends and Influence People'.
Telling stroke medical 'professionals' they know nothing about stroke is a no-no even if it is true.
Politeness will never solve anything in stroke. Yes, I'm a bomb thrower and proud of it. Someday a stroke 'leader' will try to ream me out for making them look bad by being truthful , I look forward to that day.
Assessment of Neurological Impairment and Recovery Using Statistical Models of Neurologically Healthy Behavior
Abstract
While
many areas of medicine have benefited from the development of objective
assessment tools and biomarkers, there have been comparatively few
improvements in techniques used to assess brain function and
dysfunction. Brain functions such as perception, cognition, and motor
control are commonly measured using criteria-based, ordinal scales which
can be coarse, have floor/ceiling effects, and often lack the precision
to detect change. There is growing recognition that kinematic and
kinetic-based measures are needed to quantify impairments following
neurological injury such as stroke, in particular for clinical research
and clinical trials. This paper will first consider the challenges with
using criteria-based ordinal scales to quantify impairment and recovery.
We then describe how kinematic-based measures can overcome many of
these challenges and highlight a statistical approach to quantify
kinematic measures of behavior based on performance of neurologically
healthy individuals. We illustrate this approach with a visually-guided
reaching task to highlight measures of impairment for individuals
following stroke. Finally, there has been considerable controversy about
the calculation of motor recovery following stroke. Here, we highlight
how our statistical-based approach can provide an effective estimate of
impairment and recovery.
Introduction
In
most areas of medicine, there has been rapid and continued improvement
in clinical tools and biomarkers to quantify body function and
dysfunction. Blood, urine, and saliva samples provide a wealth of
information on the function of many organs, and imaging techniques
provide detailed information on their structure. In contrast, there has
been relatively limited improvement in assessment of brain function.
Some indirect measures have been developed to look at patterns of
activity in the brain (or muscle) such as electroencephalography
(electromyography), positron emission tomography, and functional
magnetic resonance imaging. However, techniques to assess perception,
cognition, and motor impairments have changed very little over the
years. For example, assessment of motor impairments continues to be
largely based on visual and physical inspection by a clinician. These
approaches have been honed over the years to focus on key problems for a
given patient group, and commonly use criteria-based, ordinal scales to
quantify impairments. Such techniques have minimal cost (beyond the
clinician’s time) and exploit the impressive capability of our visual
system to identify atypical performance such as slight asymmetry of
gait, indicative of a stroke.
While
criteria-based, ordinal scales may be useful in the clinic for making
treatment decisions, their use is more problematic when used for
clinical research and clinical trials (e.g., the modified Rankin Scale
or Fugl-Meyer (FM) Assessment). The challenge is that our visual system
may have evolved to identify atypical behavior, but we are only able to
crudely quantify severity of atypical behavior from visual inspection,1
limiting the levels of impairment that can be reliably defined. Thus,
there is a clear need for approaches to assess objectively, reliably and
with high precision, neurological impairments and changes in
impairments due to stroke and other neurological injuries/diseases.
There
is growing recognition that kinematic and kinetic-based measures can
provide excellent quantification of impairments after stroke.2-6
A primary challenge remains how to appropriately characterize these
impairments compared to healthy control performance. Age, sex, and
handedness can often impact kinematic performance. Many studies manage
these effects by using age and sex-matched controls, permitting
statistical comparison for differences between patient cohorts and
healthy controls (group effects).7-10
However, comparisons to a healthy mean do not allow one to identify if
an individual with stroke is impaired (outside the range of performance
expected for healthy individuals). Another approach is to compare
individual patients to a distribution of healthy individuals. Cortes et
al (2017)11
compared reaching performance of individual participants with stroke to
12 healthy individuals of a similar age distribution making reaches
with their dominant hand. They calculated the Mahalanobis distance to
measure the distance of each individual patient’s reaching performance
from the distribution of healthy reaches in order to quantify
impairment. This is a step in the right direction. However, using a very
small number of age-matched individuals to estimate the performance of a
healthy population is problematic as there can be substantial deviation
between the estimated and actual distributions. This approach also does
not account for the impact of age, sex, or handedness on performance.
In
this article, we introduce a statistical approach for quantifying
impairment based on measures of neurologically healthy individuals. We
first review some of the challenges with using criteria-based ordinal
scales for quantifying impairments and recovery in order to highlight
how kinematic-based measures can overcome at least some of these
challenges. We then describe our approach that develops a statistical
model of healthy performance using a large number of healthy controls
and then use this model to transform performance of individuals with
stroke into standardized units. We illustrate our approach using a
visually-guided reaching task. However, it is important to recognize
that our approach can be used with any spatial or temporal features of
motor performance, collected with wearable sensors, markered or
markerless motion capture, or even as simple as the time to walk 10 m.
Finally, there has been considerable controversy and debate on how to
quantify motor recovery following stroke.12-18
Here we highlight how we can use our statistical-based approach to
provide a simple and effective estimate of recovery for a cohort of
individuals.
More at link.
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