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Record W4385827407 · doi:10.1093/ptj/pzad109

Assessing the Measurement Properties of the Self-Administered Amyotrophic Lateral Sclerosis Functional Rating Scale–Revised (ALSFRS-R): A Rasch Analysis

2023· article· en· W4385827407 on OpenAlexaffabout
Ava Mehdipour, Lizabeth Teshler, Vanina Dal Bello‐Haas, Julie Richardson, Marla Beauchamp, John Turnbull, Marvin Chum, Wendy Johnston, Colleen O’Connell, Westerly Luth, Ayse Kuspinar

Bibliographic record

VenuePhysical Therapy · 2023
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsStan Cassidy FoundationUniversity of AlbertaMcMaster University
Fundersnot available
KeywordsRasch modelDifferential item functioningAmyotrophic lateral sclerosisRating scalePsychologyReliability (semiconductor)Item response theoryClinical psychologyPsychometricsDiseaseMedicineDevelopmental psychologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The self-administered version of the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) is used to monitor function and disease progression in individuals with amyotrophic lateral sclerosis (ALS). However, the performance of the self-administered ALSFRS-R has not been assessed using Rasch Measurement Theory. Therefore, the purpose of this study was to examine the psychometric properties of the self-administered ALSFRS-R using Rasch analysis. METHODS: Rasch analysis was performed on self-administered ALSFRS-R data from individuals with ALS across Canada. The following 6 aspects of Rasch analysis were examined using RUMM2030: fit via residuals and chi-square statistics, targeting via person-item threshold maps, dependency via item residual correlations, unidimensionality through principal components analysis of residuals, reliability via person separation index, and stability through differential item functioning analyses for sex, age, and language. RESULTS: Analysis was performed on 122 participants (mean age: 52.9 years; 62.8% men). The overall scale demonstrated good fit, reliability, and stability; however, multidimensionality was found. To address this issue, items were divided into 3 subscales (bulbar, motor, and respiratory function), and Rasch analysis was performed for each subscale. The subscales demonstrated good fit, reliability, stability, and unidimensionality. However, there were still issues with item dependency for all subscale and targeting for bulbar and respiratory subscales. CONCLUSIONS: The self-administered ALSFRS-R is reliable, internally valid, and stable across sex, age, and language subgroups; however, it is recommended that the ALSFRS-R be scored by subscale. Future studies can look at revising and/or adding items to tackle misfit, redundancy, and ceiling effects. IMPACT: Self-administered measures are simple to administer and inexpensive. The self-administered ALSFRS-R was found to be psychometrically sound and can be used as a tool to monitor disease progression and function in ALS.

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)0.024
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.256
GPT teacher head0.343
Teacher spread0.087 · 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 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

Citations3
Published2023
Admission routes2
Has abstractyes

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