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Record W2148482689 · doi:10.1177/1545968311400093

Equivalent Retention of Gains at 1 Year After Training With Constraint-Induced or Bimanual Therapy in Children With Unilateral Cerebral Palsy

2011· article· en· W2148482689 on OpenAlexaboutno aff
Leanne Sakzewski, Jenny Ziviani, David F. Abbott, Richard Macdonell, Graeme D. Jackson, Roslyn N. Boyd

Bibliographic record

VenueNeurorehabilitation and neural repair · 2011
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsConstraint-induced movement therapyCerebral palsyPhysical therapyPhysical medicine and rehabilitationMedicineRandomized controlled trialOccupational therapyRehabilitationPsychologySurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine retention of treatment outcomes at 52 weeks following a matched-pairs randomized comparison trial of constraint-induced movement therapy (CIMT) and bimanual training (BIM). METHODS: Sixty-four children (mean age = 10.2 ± 2.7 years, 52% male) were included. The Melbourne Assessment of Unilateral Upper Limb Function (MUUL), Assisting Hand Assessment (AHA), and Canadian Occupational Performance Measure (COPM) were the primary outcome measures. Evaluations were at baseline and at 26 and 52 weeks. RESULTS: There were no baseline differences between groups on any measure. No significant differences were found between groups on primary outcomes at 52 weeks. Both groups retained the significant gains made from baseline to 26 weeks at the 1-year follow-up assessment for unimanual capacity on the MUUL, for bimanual performance on the AHA, and on the COPM. CONCLUSION: Intensive unimanual and bimanual training can both lead to long-term significant improvements in unimanual capacity, bimanual performance, and individualized outcomes. Gains established at 26 weeks were maintained at 12 months postintervention despite most children receiving no direct therapy during that time.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.063
GPT teacher head0.283
Teacher spread0.219 · 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 teacher head, 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

Citations58
Published2011
Admission routes1
Has abstractyes

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