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Record W2060669357 · doi:10.1097/pep.0b013e3181728a8c

Stretching with Children with Cerebral Palsy: What Do We Know and Where Are We Going?

2008· review· en· W2060669357 on OpenAlexaff
Lesley Pritchard, Johanna Darrah, Gayatri Kembhavi

Bibliographic record

VenuePediatric Physical Therapy · 2008
Typereview
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCerebral palsyFlexibility (engineering)RecreationPhysical medicine and rehabilitationContracturePhysical therapyPsychologyMedicineSurgery

Abstract

fetched live from OpenAlex

In Brief Purpose: To review research regarding mechanisms of muscle contracture in cerebral palsy (CP) and the effectiveness of stretching, and to discuss current physical therapy stretching practices. Community-based recreation opportunities that encourage flexibility and fitness are explored as alternatives to traditional therapy stretching approaches. Summary of Key Points: Mechanisms of muscle contracture in children with CP are unclear and clinical research evaluating the effects of stretching is inconclusive. Recent shifts in thinking about the management of children with CP suggest an increased emphasis on flexibility, fitness, and participation in activities that are meaningful to children and families. Statement of Conclusions: Additional research is needed to explore the structural changes that occur in the shortened muscles of children with CP and the effects of stretching practices used in pediatric physical therapy. Recommendations for Clinical Practice: Physical therapists can consider innovative alternatives that integrate flexibility and fitness goals with community-based recreation programs. After a review of research on muscle contracture in cerebral palsy and the effectiveness of stretching, the authors discuss current physical therapy stretching practices and suggest that community-based fitness opportunities that encourage flexibility and fitness may be an alternative to traditional stretching approaches.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.292
Teacher spread0.269 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations107
Published2008
Admission routes1
Has abstractyes

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