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Record W2105464395 · doi:10.1300/j006v24n01_05

Changes in Mobility of Children with Cerebral Palsy Over Time and Across Environmental Settings

2004· article· en· W2105464395 on OpenAlexaff
Beth Tieman, Robert J. Palisano, Edward J. Gracely, Peter Rosenbaum, Lisa A. Chiarello, Margaret E. O’Neil

Bibliographic record

VenuePhysical & Occupational Therapy In Pediatrics · 2004
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcMaster UniversityEducation and Early Childhood Development
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentU.S. Public Health Service
KeywordsCerebral palsyGross Motor Function Classification SystemGross motor skillPhysical medicine and rehabilitationPsychological interventionPsychologyMedicineConfidence intervalPhysical therapyDevelopmental psychologyMotor skillPsychiatry

Abstract

fetched live from OpenAlex

This study examined changes in mobility methods of children with cerebral palsy (CP) over time and across environmental settings. Sixty-two children with CP, ages 6-14 years and classified as levels II-IV on the Gross Motor Function Classification System, were randomly selected from a larger data base and followed for three to four years. On each of several assessments, parents completed a questionnaire on their child's usual mobility methods in the home, school, and outdoors/community settings. During the first assessment interval, mobility methods increased to methods requiring more gross motor control. During the second assessment interval, mobility methods were unchanged or decreased to methods requiring less gross motor control. Changes within the child and within the environment are hypothesized to occur and to impact changes in mobility methods. Screening at regular intervals is recommended to monitor changes in mobility. Interventions to enhance mobility may be indicated during periods of change in the child or exposure to new environments.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.451

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 head0.011
GPT teacher head0.282
Teacher spread0.271 · 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

Citations40
Published2004
Admission routes1
Has abstractyes

Explore more

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