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Use of the GMFCS in infants with CP: the need for reclassification at age 2 years or older

2008· article· en· W2029260543 on OpenAlexafffund
Jan Willem Gorter, Marjolijn Ketelaar, Peter Rosenbaum, Paul J.M. Helders, Robert J. Palisano

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2008
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcMaster University
FundersNational Center for Medical Rehabilitation ResearchEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentNational Institutes of HealthZonMwMedical Research Council CanadaMedical Research CouncilMcMaster University
KeywordsGross Motor Function Classification SystemMcNemar's testSpasticMedicineCerebral palsyConfidence intervalPediatricsPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

The stability of the Gross Motor Function Classification System (GMFCS) over time is described in 77 infants (41 boys, 36 girls) with cerebral palsy (CP; mean age 19.4mo [SD 1.6 mo]; 27 unilateral spastic, 42 bilateral spastic, eight dyskinetic type) and in the same children at follow-up at age 2 to 4 years. The overall level of agreement over time (linear weighted kappa) was 0.70 (95% confidence interval [CI] 0.61-0.79). The overall percentage of children whose GMFCS level changed one or two levels was 42%, of which the majority were reclassified to a less functional level (McNemar's Chi(2) test p=0.11). The chance that children initially classified in the combination of GMFCS Levels I, II, and III would subsequently be classified in the same level in early childhood was 96% (positive predictive value [PPV] 0.96, 95% CI 0.85-0.99), whereas the PPV for the combination of Levels I and II was 0.88, 95% CI 0.70-0.96. These findings indicate that GMFCS classification in infants is less precise than classification over time in older children. In conclusion, children can be classified by the GMFCS early on, but there is a need for reclassification at age 2 or older as more clinical information becomes available.

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 score0.020
Threshold uncertainty score0.276

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 head0.048
GPT teacher head0.260
Teacher spread0.211 · 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

Citations147
Published2008
Admission routes2
Has abstractyes

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Same venueDevelopmental Medicine & Child NeurologySame topicCerebral Palsy and Movement DisordersFrench-language works237,207