MétaCan
Menu
Back to cohort
Record W2973035061 · doi:10.1080/17518423.2019.1662853

Combined Effects of Mirror Therapy and Exercises on the Upper Extremities in Children with Unilateral Cerebral Palsy: A Randomized Controlled Trial

2019· article· en· W2973035061 on OpenAlexaboutno aff
Özgün Kaya Kara, Bilge Nur Yardımcı-Lokmanoğlu, Sedef Şahın, Ceren Orhan, Ayşe Livanelioğlu, Abdullah Ruhi Soylu

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2019
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsIsometric exerciseMedicineCerebral palsyPhysical therapyBicepsRandomized controlled trialPhysical medicine and rehabilitationUpper limbMuscle strengthRehabilitationSurgery

Abstract

fetched live from OpenAlex

Purpose: This study investigated the combined effects of mirror therapy involving power and strength exercises of the upper extremities in children with Unilateral Spastic Cerebral Palsy (USCP).Methods: Thirty children were included in either an experimental group or a control group. All participants were evaluated with the Quality of Upper Extremity Skill Test (QUEST), Canadian Occupational Performance Measure (COPM), and a handheld dynamometer to measure isometric muscle strength.Results: Compared to the control group, greater improvement was found in dissociated movements (p < .001, d = 1.82), grasp (p < .001, d = 1.38), weight bearing (p = .006, d = 0.91), and total scores (p = .001, d = 1.16) of QUEST; performance (p < .001, d = 2.9), satisfaction (p < .001, d = 1.91), and total scores (p < .001, d = 2.87) of COPM; and isometric muscle strength of the biceps brachii (p < .001, d = 1.27) and triceps brachii (p = .002, d = 2.22) of the affected upper limbs in the experimental group.Conclusions: Mirror therapy combined with power and strength exercises is a promising intervention approach to improve activity performance and upper-limb function in children with USCP.

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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
GPT teacher head0.220
Teacher spread0.212 · 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 designRandomized 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

Citations28
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueDevelopmental NeurorehabilitationSame topicCerebral Palsy and Movement DisordersFrench-language works237,207