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Record W2018380776 · doi:10.1177/0883073814533152

Health-Related Physical Fitness for Children With Cerebral Palsy

2014· review· en· W2018380776 on OpenAlexaff
Désirée B. Maltais, Lesley Pritchard, Eileen Fowler, Olaf Verschuren, Diane L. Damiano

Bibliographic record

VenueJournal of Child Neurology · 2014
Typereview
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsGlenrose Rehabilitation HospitalAlberta Health ServicesUniversité LavalUniversity of AlbertaCentre for Interdisciplinary Research in Rehabilitation
FundersNational Institutes of Health
KeywordsCerebral palsyPhysical fitnessPhysical medicine and rehabilitationPhysical therapyPhysical activityMedicinePhysical examinationPsychology

Abstract

fetched live from OpenAlex

Low levels of physical activity are a global health concern for all children. Children with cerebral palsy have even lower physical activity levels than their typically developing peers. Low levels of physical activity, and thus an increased risk for related chronic diseases, are associated with deficits in health-related physical fitness. Recent research has provided therapists with the resources to effectively perform physical fitness testing and physical activity training in clinical settings with children who have cerebral palsy, although most testing and training data to date pertains to those who walk. Nevertheless, on the basis of the present evidence, all children with cerebral palsy should engage, to the extent they are able, in aerobic, anaerobic, and muscle-strengthening activities. Future research is required to determine the best ways to evaluate health-related physical fitness in nonambulatory children with cerebral palsy and foster long-term changes in physical activity behavior in all children with this condition.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.019
GPT teacher head0.313
Teacher spread0.293 · 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 designNot applicable
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

Citations76
Published2014
Admission routes1
Has abstractyes

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