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Associations between sensorimotor impairments and motor ability deficits: the pieces of the puzzle are beginning to fall into place

2010· letter· en· W2002169713 on OpenAlexaff
Mindy F. Levin

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2010
Typeletter
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyCerebral palsyCognitive psychologyMotor skillCorrelationMotor functionPhysical medicine and rehabilitationDevelopmental psychologyMedicine

Abstract

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Many studies on children with cerebral palsy (CP) have provided elegant and detailed descriptions of sensorimotor impairments (also referred to as negative and positive signs of upper motor neuron lesions) and have sought to describe associations between these impairments and deficits in motor abilities. However, efforts in the past to find quantifiable and clinically meaningful relationships between deficits in sensorimotor function and activity limitations have largely been elusive. A metaphor for this situation is that each identified impairment is like one piece of a large jigsaw puzzle. Although the size and shape of each puzzle piece can be clearly characterized, the question of how to orient each piece in order to determine how it contributes to the larger picture – in this case the motor function or activity – is much harder to resolve. Sakzewski et al.1 have taken an important positive step in this direction. In their cross-sectional study of 70 children with hemiplegic CP, two important findings emerge – a moderate correlation between sensory deficits in the hand and unimanual motor capacity; and a strong correlation between unimanual capacity and bimanual performance. Why have these authors found these relationships when others have failed? Several factors may have been at play here. One factor is that the study included a large enough sample of participants so that meaningful statistical and clinical relationships could be found using appropriate statistical models. Another key element to their success is the use of the unifying theoretical framework of the International Classification of Functioning, Disability and Health (ICF) within which to arrange their puzzle pieces.2 The ICF not only provides a structure for classification of components of mobility but also a means to establish clear terminology within and between hierarchical levels. Indeed, the structure of the ICF itself implies relationships between elements at each level and between levels without specifying the exact nature of these relationships. Sakzewski et al.1 chose valid and reliable outcome measures reflecting a broad spectrum of functioning, that were effective measures of their desired constructs at two levels of the ICF framework. By their judicious use of outcome measures chosen to address specific aspects of motor function within the framework of the ICF, Sakzewski et al. were able to describe these relationships with respect to impairments and activities at the arm and hand level. Their second major finding, the relationship between unimanual and bimanual arm function, was found using two recently described measures – the Melbourne Assessment of Unilateral Upper Limb Function3 and the Assisting Hand Assessment.4 These two measures have undergone rigorous development and validity and reliability testing in the population investigated. More importantly, both measures are based on constructs that have ecological validity, and thus they may better reflect the desired constructs than earlier clinical measures. The use of these measures may help explain why the previous elusive relationship between unimanual and bimanual deficits can now be described. The results of Sakzewski et al.’s study represent an important step forward in our understanding of the relationship between arm impairments, unimanual function, and bimanual performance in children with hemiplegia. Insights into these relationships can be used by clinicians to guide treatment interventions for improving arm and hand function. The next essential step is to determine if, indeed, training of the impaired upper limb will impact bimanual performance in a meaningful way leading to greater participation of the child across the spectrum of life activities.5

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0010.002
Scholarly communication0.0020.006
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.251
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2010
Admission routes1
Has abstractyes

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