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Record W4246752635 · doi:10.1300/j006v27n01_02

An Investigation of Bilateral Isokinematic Training and Neurodevelopmental Therapy in Improving Use of the Affected Hand in Children with Hemiplegia

2007· article· en· W4246752635 on OpenAlexaboutno aff
Loretta Sheppard, Heather Mudie, Elspeth Froude

Bibliographic record

VenuePhysical & Occupational Therapy In Pediatrics · 2007
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical medicine and rehabilitationOccupational therapyMedicinePhysical therapyPsychologyRehabilitation

Abstract

fetched live from OpenAlex

Motor impairment in children with hemiplegic cerebral palsy leads to a predominance of use of the unaffected hand. This impedes development of bimanual skills and deprives the affected side of the stimulus needed for normal growth. Occupational therapists aim to improve use of the affected hand, traditionally using Neurodevelopmental Therapy. Empirical evidence is needed to support this treatment choice. Studies examining interlimb coupling in children with hemiplegia and other studies in adult stroke support a bilateral treatment approach. These single-case time-series experiments examined the effects of Neurode-Loretta Sheppard, MOT, BAppSc(OT), is Occupational Therapist at Ballarat Specialist School. velopmental Therapy and Bilateral Isokinematic Training on hand use in three children with hemiplegic cerebral palsy. Two of the three subjects displayed positive changes in use and movement of the affected hand in some tasks with Bilateral Isokinematic Training. Bilateral Isokinematic Training might be a potentially useful means of increasing frequency of use of the affected hand in some children with hemiplegic cerebral palsy.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0010.000

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 head0.032
GPT teacher head0.288
Teacher spread0.256 · 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 designNon-randomized trial
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
Published2007
Admission routes1
Has abstractyes

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