MétaCan
Menu
Back to cohort
Record W2909869969 · doi:10.1177/1179572718823510

Evaluation of the Multiple Sclerosis Spasticity Scale 88: A Short Report

2019· article· en· W2909869969 on OpenAlexfundno aff
Jenny Freeman, Terry Gorst, Jodielin Ofori, Jonathan Marsden

Bibliographic record

VenueRehabilitation Process and Outcome · 2019
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsnot available
FundersMinistère de la Santé et des Services sociaux
KeywordsSpasticityModified Ashworth scaleMultiple sclerosisPhysical therapyPhysical medicine and rehabilitationAnkleMedicineExpanded Disability Status ScalePsychologyPsychiatrySurgery

Abstract

fetched live from OpenAlex

Background: The Multiple Sclerosis Spasticity Scale 88 (MSSS-88) is designed to capture the patient experience and impact of spasticity, but there is limited evaluation against clinician-rated measures of spasticity. Objective: To evaluate the convergent validity and responsiveness of the MSSS-88. Design: Longitudinal study. Setting: University Laboratory. Subjects: Thirty-four people with multiple sclerosis. Methods: People with multiple sclerosis (MS; n = 34) completed the self-reported 12-item Multiple Sclerosis Walking Scale, Multiple Sclerosis Spasticity Scale, Barthel Index alongside the clinician-rated Ashworth Scale, and a laboratory-based measure of ankle spasticity. Spasticity measure responsiveness was evaluated in 20 participants at two time points, an average of 8.75 ± 3.8 months apart. Results: In people with MS (mean age 55.1 ± 8.1 years; Expanded Disability Scale range 4.5-7.0), spasticity symptom specific subscales of the MSSS-88 (stiffness and spasms) showed strong and significant correlations with the clinician-rated Ashworth Scale ( r = 0.52-0.53; P < .01). Responsiveness of the MSSS-88 was comparable to a laboratory-based measure of ankle spasticity. Conclusions: Our findings lend additional support to the convergent validity of this measure.

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)0.002
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 score0.007
Threshold uncertainty score0.203

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.049
GPT teacher head0.319
Teacher spread0.270 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueRehabilitation Process and OutcomeSame topicBotulinum Toxin and Related Neurological DisordersFrench-language works237,207