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Record W2463210585 · doi:10.1097/pep.0000000000000269

Effectiveness of Stretch Interventions for Children With Neuromuscular Disabilities: Evidence-Based Recommendations

2016· review· en· W2463210585 on OpenAlexafffund
Jason Craig, Courtney Hilderman, Geoffrey Wilson, Robyn Misovic

Bibliographic record

VenuePediatric Physical Therapy · 2016
Typereview
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsIsland Health
FundersUniversity of British Columbia
KeywordsPhysical medicine and rehabilitationPsychological interventionMedicinePsychologyPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE: To determine whether casting, orthoses, stretching, or supported standing programs are effective in improving or maintaining body functions and structures, activity, or participation in children with neuromuscular disabilities. METHODS: A systematic review was conducted using 6 electronic databases to identify Level 1 and 2 studies investigating stretch interventions for children aged 0 to 19 years with neuromuscular disabilities. Interventions were coded using the International Classification of Function and rated with Grading of Recommendation Assessment, Development and Evaluation, the Oxford Levels of Evidence, and the Evidence Alert Traffic Light System. RESULTS: Sixteen studies evaluated the effectiveness of stretch interventions. Low-grade evidence supports casting temporarily increasing ankle range of motion, orthoses improving gait parameters while they are worn, and supported standing programs improving bone mineral density. CONCLUSION: There is limited evidence suggesting stretch interventions benefit body functions and structures. There is inconclusive evidence to support or refute stretching interventions for preventing contractures or impacting a child's activity or participation. TRIAL REGISTRATION: Prospero CRD42014013807.

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)0.010
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.054
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.009
Bibliometrics0.0090.006
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.092
GPT teacher head0.398
Teacher spread0.306 · 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 designSystematic review
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

Citations29
Published2016
Admission routes2
Has abstractyes

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