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Record W2761849449 · doi:10.1093/pch/20.5.e43

27: Exploring Sensorimotor Plasticity in Hemiplegic Cerebral Palsy Following Constraint-Induced Movement Therapy

2015· article· en· W2761849449 on OpenAlexaff
Sarah D’Souza, Sabah Master, C Jobst, Lauren Switzer, Douglas Cheyne, Darcy Fehlings

Bibliographic record

VenuePaediatrics & Child Health · 2015
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsHolland Bloorview Kids Rehabilitation Hospital
Fundersnot available
KeywordsPhysical medicine and rehabilitationSensory systemProprioceptionCerebral palsyConstraint-induced movement therapyMagnetoencephalographySomatosensory systemSensory stimulation therapyPsychologyStatistical parametric mappingMedicineNeuroplasticitySensationTranscranial magnetic stimulationPhysical therapyUpper limbNeuroscienceElectroencephalographyMagnetic resonance imagingStimulation

Abstract

fetched live from OpenAlex

Children with hemiplegic cerebral palsy (HCP) experience upper limb sensory processing and motor deficits. Current interventions focus on motor deficits while sensory impairments are overlooked. Movement and sensation are intimately related and theories on motor behavior support addressing both. While constraint-induced movement therapy (CIMT) has demonstrated effectiveness in improving upper limb motor function in HCP, its impact on sensory function remains unknown at both the clinical and neural level. The primary objective of this study was to evaluate the effectiveness of CIMT in improving clinical and neural sensory function in children with HCP. Ten children with HCP were recruited from the HCP registry (CP-NET). Participants attended a 3-week CIMT intervention and completed neuroimaging and clinical assessments one week before and one week after CIMT. Bilateral somatosensory evoked fields (SEFs) to tactile stimulation were recorded using magnetoencephalography (MEG) to assess neuroplastic changes in the somatosensory cortex (SI) corresponding to the affected hand (measured by mean peak amplitude of the SEF). Clinical sensory assessments included: two-point discrimination (2PD), tactile registration, stereognosis (to assess tactile function), proprioception and kinesthesia tests (body motion awareness). Paired parametric and non-parametric tests assessed changes in clinical sensory measures. Voxel-wise permutation analysis evaluated significant differences in brain activity in the region of interest of the S1 cortex, pre- and post- CIMT. Post CIMT, there was a reduction in mean target degree error of the proprioceptive task (4.6 degrees, P=0.03) indicating an increase in joint position sense accuracy. Other clinical sensory measures including tactile registration of the index finger, 2PD and kinesthesia showed trends towards improvement post CIMT. MEG data revealed a significant post-CIMT increase (P≤0.05, moment=5.57 nanoamperes) SEF amplitude at a latency of 50 milliseconds corresponding to neuronal changes within the affected S1 post central gyrus (BA3) contralateral to the affected hand. This study found improved proprioception in children with HCP after CIMT along with positive trends in other sensory modalities. Importantly, neuroplastic change in sensory processing of tactile information was also identified in the primary sensory area of the injured hemisphere. This study provides a basis for further research to be done on how CIMT addresses deficits in various sensory modalities. Sensory deficits should be considered a critical factor to remediate in “motor” rehabilitation programs aimed to improve functioning.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.074
GPT teacher head0.302
Teacher spread0.228 · 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
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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