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Record W1978657498 · doi:10.1177/0883073812463608

The Relationship Between Gross Motor Function and Manual Ability in Cerebral Palsy

2012· article· en· W1978657498 on OpenAlexaffabout
Maryam Oskoui, Annette Majnemer, Lynn Dagenais, Michael Shevell

Bibliographic record

VenueJournal of Child Neurology · 2012
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcGill UniversityMontreal Children's Hospital
Fundersnot available
KeywordsCerebral palsySpastic diplegiaGross Motor Function Classification SystemGross motor skillSpasticPhysical medicine and rehabilitationPsychologyMedicineCorrelationDiplegiaRetrospective cohort studyCognitionSpastic cerebral palsyPhysical therapyMotor skillAudiologyPediatricsDevelopmental psychologyNeuroscienceSurgery

Abstract

fetched live from OpenAlex

A retrospective cohort study was conducted to describe the relationship between gross motor function and manual ability in children with cerebral palsy and explore differences between cerebral palsy subtypes and associated comorbidities. Children with cerebral palsy born between 1999 and 2008 were included from the Registre de la Paralyse Cérébrale de Québec identifying 332 children. The overall agreement between Gross Motor Function Classification System and Manual Ability Classification Scale Levels was moderate (kappa 0.457, standard error 0.034) with a strong positive correlation (Spearman rho of 0.820, standard error 0.023). This agreement was moderate among children with spastic quadriparesis and dysketic cerebral palsy, fair in children with spastic diplegia, and poor in children with spastic hemiplegia. Children with cognitive impairment showed a higher correlation than those without cognitive impairment. The correlation between gross motor function and manual ability in children with CP varies based on neurologic subtype and cognitive level.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.101
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.284
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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

Citations24
Published2012
Admission routes2
Has abstractyes

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