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Reliability of classification of cerebral palsy in low‐birthweight children in four countries

2003· article· en· W4252266312 on OpenAlexaffabout
Nigel Paneth, Hongqiang Qiu, Saroj Saigal, Sharif Bishai, James Jetton BS, Lya Den, Sue Broyles, Jon E. Tyson, Karl Kugler

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2003
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsCerebral palsyGross Motor Function Classification SystemKappaPediatricsPopulationReliability (semiconductor)MedicineCohortPsychologyPhysical therapyMathematicsInternal medicine

Abstract

fetched live from OpenAlex

The reliability of classification of cerebral palsy (CP) in low‐birth weight children was assessed by using clinical and research study records sampled from population‐based cohort studies in the USA, the Netherlands, Canada, and Germany. Records of neurological examination findings and functional motor assessments were submitted to up to five pediatricians with expertize in CP diagnosis, who grouped children into categories referred to as‘disabling’CP,‘non‐disabling’CP, and no CP. Each study provided between 31 and 51 records of children assessed between 2 and 8 years of age, approximately equally divided among the three groupings. The discrimination between‘any CP’and‘no CP’was only fair (mean Kappa coefficients 0.37 to 0.69). However, when more detailed information describing motor function was used, children with‘disabling’CP could be distinguished, on the basis of records, from those without CP or with‘non‐disabling’CP with good to excellent reliability (mean Kappa coefficients 0.69 to 0.88). Because of the substantially higher agreement observed when these functional distinctions are made, we recommend that reports or comparisons of rates of CP should include levels of motor function of children with CP, and not simply total CP, among the outcomes of interest.

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)0.001
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 score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.241
Teacher spread0.229 · 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

Citations34
Published2003
Admission routes2
Has abstractyes

Explore more

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