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Record W2019622320 · doi:10.1080/17518420902936722

Screening for motor deficits using the Pediatric Evaluation of Disability Inventory (PEDI) in children with language impairment

2009· article· en· W2019622320 on OpenAlexaff
L. Mayrand, Barbara Mazer, Suzanne Ménard, Gevorg Chilingaryan

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2009
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in RehabilitationJewish Rehabilitation Hospital
Fundersnot available
KeywordsGross motor skillMotor impairmentMotor skillPsychologyAudiologyMotor dysfunctionPhysical medicine and rehabilitationPhysical therapyMedicineDevelopmental psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the association between the Pediatric Evaluation of Disability Inventory (PEDI) motor and self-care domains with the Peabody Developmental Motor Scales-second edition (PDMS-2) gross motor and fine motor sub-scales. METHODS: Forty children (35-62 months) with primary language impairment (PLI) were recruited. The PEDI was completed at admission and the PDMS-2 was administered within 1 month by an OT, who was unaware of the PEDI results. RESULTS: Correlation between PEDI mobility and PDMS-2 gross motor domains was r = 0.23 (p = 0.15) and between PEDI self-care and PDMS-2 fine motor domains was r = 0.12 (p = 0.47). Associations between PEDI and PDMS-2 scores for age, gender and severity of language impairment sub-groups were poor-to-moderate. CONCLUSION: Findings indicate the PEDI is not sufficiently accurate to screen for motor deficits in children with PLI. More sensitive measures of motor performance are needed to detect subtle motor deficits in children with PLI.

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)0.000
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 score0.017
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.029
GPT teacher head0.305
Teacher spread0.276 · 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

Citations15
Published2009
Admission routes1
Has abstractyes

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