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Record W2010989450 · doi:10.1017/s0012162200000529

The Gross Motor Function Classification System for Cerebral Palsy: a study of reliability and stability over time

2000· article· en· W2010989450 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2000
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcMaster UniversityDalhousie University
Fundersnot available
KeywordsGross Motor Function Classification SystemCerebral palsyInter-rater reliabilityReliability (semiconductor)Physical therapyPsychologyPhysical medicine and rehabilitationMotor functionPredictive valueMedicineRating scaleDevelopmental psychology

Abstract

fetched live from OpenAlex

Children with cerebral palsy (CP) experience a change in motor function with age and development. It is important to consider this expected change in offering a prognosis, or in assessing differences in motor function after an intervention. The Gross Motor Function Classification System for CP (GMFCS) has been developed for these purposes. This study was based on a retrospective chart review of 85 children with CP followed from < or =2 to > or =12 years of age. The GMFCS was applied to clinical notes by two blinded raters four times throughout the study. Interrater reliability was high (G=0.93). Test-retest reliability was high (G=0.79). The positive predictive value of the GMFCS at 1 to 2 years of age to predict walking by age 12 years was 0.74. The negative predictive value was 0.90. The GMFCS can validly predict motor function for children with CP. The results are discussed in terms of their implications for clinical practice and future research.

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.

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)0.001
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 score0.088
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.246
Teacher spread0.230 · 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