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Record W3193443689 · doi:10.1097/pep.0000000000000829

Scootering for Children and Youth Is More Than Fun: Exploration of a Feasible Approach to Improve Function and Fitness

2021· article· en· W3193443689 on OpenAlexaff
Marilyn Wright, Donna Twose, Jan Willem Gorter

Bibliographic record

VenuePediatric Physical Therapy · 2021
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcMaster Children's HospitalMcMaster University Medical Centre
Fundersnot available
KeywordsCerebral palsyIntervention (counseling)PsychologyMotion (physics)Physical medicine and rehabilitationRehabilitationGaitDevelopmental psychologyFunction (biology)Physical therapyMedicineComputer sciencePsychiatryArtificial intelligence

Abstract

fetched live from OpenAlex

PURPOSE: Describe scootering as a physical therapy intervention for children/adolescents with mobility limitations within the "F-Words for Child Development" (fitness, function, family, friends, fun, and future) and through motion analysis. METHODS: Perspectives of scootering were explored using the holistic "F-words for Child Development" recommendations for pediatric rehabilitation and through 3-dimensional instrumented motion analysis of children/adolescents with cerebral palsy and children/adolescents with typical development. RESULTS: Scootering was consistent with the F-words tenets for rehabilitative best practice. Many of the motion characteristics of scootering reflected desirable exercise and gait attributes relevant to children/adolescents with cerebral palsy. CONCLUSIONS: Scootering is a feasible, functional, and fun activity that has the potential to address many aspects of fitness, function, and gait; meet the needs of families; and provide opportunities for interaction with friends. It is a physical therapy intervention that has the potential to contribute to future health and well-being of children with disabilities. VIDEO ABSTRACT: For more insights from the authors, see Supplemental Digital Content 1, available at: http://links.lww.com/PPT/A331.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.035
GPT teacher head0.281
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 teacher head, 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

Citations1
Published2021
Admission routes1
Has abstractyes

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