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Record W2147476283 · doi:10.1155/2014/730298

Rasch Analysis of a New Hierarchical Scoring System for Evaluating Hand Function on the Motor Assessment Scale for Stroke

2014· article· en· W2147476283 on OpenAlexaboutno aff
Joyce S. Sabari, Michelle Woodbury, Craig A. Velozo

Bibliographic record

VenueStroke Research and Treatment · 2014
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
FundersJohns Hopkins University
KeywordsRasch modelRehabilitationScale (ratio)Reliability (semiconductor)Stroke (engine)Physical medicine and rehabilitationPhysical therapyMedicinePsychologyDevelopmental psychologyCartography

Abstract

fetched live from OpenAlex

Objectives. (1) To develop two independent measurement scales for use as items assessing hand movements and hand activities within the Motor Assessment Scale (MAS), an existing instrument used for clinical assessment of motor performance in stroke survivors; (2) To examine the psychometric properties of these new measurement scales. Design. Scale development, followed by a multicenter observational study. Setting. Inpatient and outpatient occupational therapy programs in eight hospital and rehabilitation facilities in the United States and Canada. Participants. Patients (N = 332) receiving stroke rehabilitation following left (52%) or right (48%) cerebrovascular accident; mean age 64.2 years (sd 15); median 1 month since stroke onset. Intervention. Not applicable. Main Outcome Measures. Data were tested for unidimensionality and reliability, and behavioral criteria were ordered according to difficulty level with Rasch analysis. Results. The new scales assessing hand movements and hand activities met Rasch expectations of unidimensionality and reliability. Conclusion. Following a multistep process of test development, analysis, and refinement, we have redesigned the two scales that comprise the hand function items on the MAS. The hand movement scale contains an empirically validated 10-behavior hierarchy and the hand activities item contains an empirically validated 8-behavior hierarchy.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.120
GPT teacher head0.437
Teacher spread0.317 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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