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Record W2587389319 · doi:10.3233/nre-161405

Inter-rater reliability of the Chedoke Arm and Hand Activity Inventory

2017· article· en· W2587389319 on OpenAlexaff
Denise Johnson, Jocelyn E. Harris, Paul W. Stratford, Julie Richardson

Bibliographic record

VenueNeurorehabilitation · 2017
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsPopulationReliability (semiconductor)Observational studyStandard errorStroke (engine)Inter-rater reliabilityPhysical therapyPhysical medicine and rehabilitationPsychologyMedicineStatisticsMathematicsDevelopmental psychologyRating scale

Abstract

fetched live from OpenAlex

BACKGROUND: The Chedoke Arm and Hand Activity Inventory (CAHAI) is an assessment of upper limb function designed for use in the stroke population. The CAHAI has strong reliability and validity in this population; however, it is unknown whether this measure can be used with other clinical populations such as acquired brain injury (ABI). PURPOSE: The purpose of this study was to estimate the inter-rater reliability of the CAHAI when used with persons with ABI. METHODS: The research design was an observational parameter estimation study. The administration of the CAHAI was videotaped for 6 persons with ABI. To estimate inter-rater reliability each video was assessed independently by 6 clinicians yielding a total of 36 assessments. A Latin square design was used to balance the order raters evaluated the videos. Shrout and Fleiss Type 2,1 intra class correlation coefficients (ICC) and standard error of measurement (SEM) were calculated to estimate inter-rater reliability of the CAHAI. RESULTS: Inter-rater reliability was high ICC = 0.96 (95% CL: 0.88, 0.99) and the SEM was 3.35 (95% CL: 2.63, 4.63) CAHAI points. CONCLUSIONS: These results suggest that the CAHAI, although designed for use in the stroke population, can be used reliably in the ABI population.

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)0.004
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 score0.050
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.020
GPT teacher head0.294
Teacher spread0.273 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

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