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Record W2978339494 · doi:10.1080/01942638.2020.1758984

Longitudinal Trajectories and Reference Percentiles for Participation in Family and Recreational Activities of Children with Cerebral Palsy

2020· article· en· W2978339494 on OpenAlexaff
Lisa A. Chiarello, Robert J. Palisano, Lisa Avery, Steven Hanna

Bibliographic record

VenuePhysical & Occupational Therapy In Pediatrics · 2020
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsCerebral palsyGross Motor Function Classification SystemPercentileRecreationLongitudinal studyPsychologyPhysical therapyPhysical medicine and rehabilitationDevelopmental psychologyMedicineStatisticsMathematics

Abstract

fetched live from OpenAlex

AIM: To create longitudinal trajectories and reference percentiles for frequency of participation in family and recreational activities for children with cerebral palsy (CP) by Gross Motor Function Classification System (GMFCS) level. METHODS: 708 children with CP 18-months to 12-years of age and their families participated in two to five assessments using the GMFCS and Child Engagement in Daily Life Measure. Data were analyzed using mixed-effects models and quantile regression. RESULTS: Longitudinal trajectories depict the relatively stable level of frequency of participation with considerable individual variability. Average change in the frequency of participation scores of children from 2-12 years of age by GMFCS level varied from 3.7 (GMFCS level I) to - 9.0 points (GMFCS level V). A system to interpret the magnitude of change in percentiles over time is presented. CONCLUSIONS: Longitudinal trajectories and reference percentiles can inform therapists and families for collaboratively designing services and monitoring performance to support children's participation in family and recreational activities.

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 score0.360

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 head0.063
GPT teacher head0.337
Teacher spread0.275 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

Explore more

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