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Record W2106212323 · doi:10.2522/ptj.20100356

Muscle Strengthening in Children and Adolescents With Spastic Cerebral Palsy: Considerations for Future Resistance Training Protocols

2011· article· en· W2106212323 on OpenAlexaff
Olaf Verschuren, Louise Ada, Désirée B. Maltais, Jan Willem Gorter, Aline Alvim Scianni, Marjolijn Ketelaar

Bibliographic record

VenuePhysical Therapy · 2011
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsCentre for Disability Prevention and RehabilitationCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsCerebral palsySpasticPhysical medicine and rehabilitationResistance trainingSpastic cerebral palsyMedicinePhysical therapyPsychology

Abstract

fetched live from OpenAlex

UNLABELLED: Resistance training of the lower limbs is now commonly used in clinical practice in children and adolescents with spastic cerebral palsy (CP). However, the effectiveness of this type of training is still disputed. The most recently published systematic review with meta-analysis included interventions such as electrical stimulation and resistance training and found insufficient evidence to support or refute the efficacy of these exercises in children with CP. Thus, the aim of this article is to evaluate the extent to which training protocols from the most recent randomized controlled trials are in keeping with the evidence for effective resistance training in children who are developing typically, as reflected in the training guidelines of the National Strength and Conditioning Association. RECOMMENDATIONS: for resistance training protocols, based on this evidence and appropriate to children with CP, are provided to help guide both future research and clinical practice for resistance training in children with CP.

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)0.039
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.063
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0030.001
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.045
GPT teacher head0.291
Teacher spread0.246 · 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 designTheoretical or conceptual
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

Citations134
Published2011
Admission routes1
Has abstractyes

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