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Record W2150620088 · doi:10.3109/01942638.2010.489543

Development and Pilot Testing of the Challenge Module: A Proposed Adjunct to the Gross Motor Function Measure for High-Functioning Children with Cerebral Palsy

2010· article· en· W2150620088 on OpenAlexafffund
Ashlea Wilson, Abi Kavanaugh, Rosemarie Moher, Megan McInroy, Neena Gupta, Nancy M. Salbach, F. Virginia Wright

Bibliographic record

VenuePhysical & Occupational Therapy In Pediatrics · 2010
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsRasch modelCerebral palsyGross motor skillAdjunctPhysical medicine and rehabilitationMotor functionPsychologyPhysical therapyGross Motor Function Classification SystemMotor skillTest (biology)AudiologyMedicineDevelopmental psychology

Abstract

fetched live from OpenAlex

The aim was to develop a Challenge Module (CM) as a proposed adjunct to the Gross Motor Function Measure for children with cerebral palsy who have high-level motor function. Items were generated in a physiotherapist (PT) focus group. Item reduction was based on PTs' ratings of item importance and safety via online surveys. The proposed CM items were pilot-tested with children in Gross Motor Function Classification System Level I. The focus group identified 35 items for consideration. The first item-reduction survey (n = 86 PT respondents) resulted in 20 items. A second survey yielded two additional items. Seven pilot-test participants (6-14 years) had a CM total mean score of 74.5% (SD = 19.4). Three easy items were subsequently removed and two items combined. Of seven additional items suggested by the children during testing, two were accepted in a third item-reduction survey. The final result was a 20-item CM to evaluate advanced motor skills. The CM requires refinement through Rasch scaling and formal validation.

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)0.010
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.039
GPT teacher head0.274
Teacher spread0.235 · 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 designBench or experimental
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

Citations39
Published2010
Admission routes2
Has abstractyes

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