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Record W1975332768 · doi:10.3109/17518423.2012.755575

Reliability of retrospective assignment of gross motor function classification system scores

2013· article· en· W1975332768 on OpenAlexaff
Tanja A. Mayson, V. Ward, Karen Davies, Jessica Maurer, Christine M. Alvarez, Richard Beauchamp, Alec Black

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2013
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsBritish Columbia Children's HospitalSunny Hill Health Centre for Children
Fundersnot available
KeywordsGross Motor Function Classification SystemPhysical therapyKappaCerebral palsyReliability (semiconductor)GaitPhysical medicine and rehabilitationRandomized controlled trialCohen's kappaPsychologyMedicineComputer scienceSurgeryMachine learningMathematics

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess "alternate forms" reliability and inter-rater reliability of Gross Motor Function Classification System (GMFCS) scores. METHODS: Fifty randomly selected children with cerebral palsy were divided into two groups: (1) GMFCS score assigned during gait assessment ("GMFCS previously assigned") and (2) no GMFCS score assigned. Using database information, two physiotherapists independently determined GMFCS scores for 25 children from the "previously assigned" group, and 25 from the "no score assigned" group. Therapists compared their recently assigned scores for the "previously assigned" group, discussing discrepancies until attaining agreement. This group's consensus scores were compared to GMFCS scores assigned at time of actual assessment to calculate "alternate forms" reliability. RESULTS: Between-therapist agreements were kappa = 0.84 for "GMFCS previously assigned" group and 0.95 for "no GMFCS assigned" group. Kappa agreement between direct assessment and retrospectively assigned scores for the "GMFCS previously assigned" group was 0.79. CONCLUSIONS: Retrospective GMFCS scores can be reliably assigned.

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 score0.359
Threshold uncertainty score0.487

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 head0.013
GPT teacher head0.235
Teacher spread0.221 · 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

Citations9
Published2013
Admission routes1
Has abstractyes

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