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Record W2139595890 · doi:10.2522/ptj.20130214

Health-Enhancing Physical Activity in Children With Cerebral Palsy: More of the Same Is Not Enough

2013· article· en· W2139595890 on OpenAlexaff
Olaf Verschuren, Johanna Darrah, Iona Novak, Marjolijn Ketelaar, Lesley Pritchard

Bibliographic record

VenuePhysical Therapy · 2013
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsGlenrose Rehabilitation HospitalUniversity of Alberta
Fundersnot available
KeywordsCerebral palsyPhysical activitySedentary behaviorPsychologyPhysical therapyTypically developingPopulationDevelopmental psychologySedentary lifestyleMedicinePhysical medicine and rehabilitationEnvironmental health

Abstract

fetched live from OpenAlex

Physical activity is important for young people's health. The emphasis over the last 2 decades has been on moderate to vigorous exercise when designing activity and exercise programs for children and adolescents with cerebral palsy (CP). Emerging evidence suggests that sedentary behavior is distinctly different from a lack of moderate to vigorous physical activity and has independent and different physiological mechanisms. The concept of concurrently increasing moderate to vigorous physical activity and replacing sedentary behavior with light physical activity may be beneficial for children and adolescents with CP. This article is a summary of the evidence for what works and what does not work for improving the physical activity of children and adolescents with CP. It also discusses what is known about sedentary behavior of children and adolescents with CP and what research directions are needed to build foundational knowledge in this area with this population.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.292
Teacher spread0.274 · 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 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

Citations83
Published2013
Admission routes1
Has abstractyes

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