MétaCan
Menu
Back to cohort
Record W2113014893 · doi:10.1177/0269215508098892

Classification of manual abilities in children with cerebral palsy under 5 years of age: how reliable is the Manual Ability Classification System?

2009· article· en· W2113014893 on OpenAlexaff
Marjolijn Ketelaar, MG Nijnuis, L. Enkelaar, JW Gorter

Bibliographic record

VenueClinical Rehabilitation · 2009
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcMaster University
FundersZonMw
KeywordsCerebral palsyPhysical medicine and rehabilitationMedicinePhysical therapyPediatrics

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the interobserver reliability of the Manual Ability Classification System (MACS) in young children (age 1-5 years) with cerebral palsy. DESIGN: Interobserver reliability study. SETTING: A cross-sectional study of a hospital-based population of children with cerebral palsy. SUBJECTS: Thirty children, 18 boys and 12 girls between 1 and 5 years of age (mean age 2.5 years +/- 14.2 SD, Gross Motor Function Classification System level I-IV). MEASURES: the children were classified by means of the MACS by two independent observers. Interobserver reliability was analysed using Cohen's kappa. RESULTS: Overall interobserver reliability of the MACS for children aged 1-5 years was moderate, with a linear weighted kappa (kappa) of 0.62 (95% confidence interval (CI) 0.49-0.76). According to the generally accepted categories of agreement, reliability was moderate for children under 2 years of age (kappa = 0.55), and good for children between 2 and 5 years of age (kappa = 0.67). CONCLUSION: Classification of manual ability of young children with cerebral palsy is possible between 2 and 5 years of age. For children younger than 2 years old, it should be done with caution. Further development of the MACS for children under 5 years of age is recommended with an emphasis on age-appropriate descriptions of manual abilities.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.011
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.033
GPT teacher head0.339
Teacher spread0.306 · 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

Citations57
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueClinical RehabilitationSame topicCerebral Palsy and Movement DisordersFrench-language works237,207