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Record W4402582080 · doi:10.7224/1537-2073.2022-104

Occupation-Based Intervention for People With Multiple Sclerosis: A Feasibility Study

2024· article· en· W4402582080 on OpenAlexaboutno aff
Sara Afshar, Nazila Akbarfahimi, Mina Ahmadi Kahjoogh, Mehdi Rassafiani, Mojtaba Azimian, Terry K. Crowe

Bibliographic record

VenueInternational Journal of MS Care · 2024
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMultiple sclerosisIntervention (counseling)Physical therapyPhysical medicine and rehabilitationGerontologyNursingPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: This study aims to evaluate the feasibility of an occupation-based intervention (OBI) on dexterity and occupational performance for people with multiple sclerosis (MS) and to gather preliminary efficacy data. METHODS: In this feasibility study, 2 women with MS participated in 12 OBI sessions that focused on increasing upper extremity function. The Canadian Occupational Performance Measure, 9-Hole Peg Test, Expanded Disability Status Scale, Montreal Cognitive Assessment, and Fatigue Severity Scale (FSS) were used as outcome measures. The scores of these assessments are reported descriptively. RESULTS: According to preliminary data, both participants demonstrated improvements in dexterity, occupational performance, and occupational performance satisfaction. These data suggest that OBI may be implemented effectively in Iran. CONCLUSIONS: OBI improved the functional use of the participants' upper extremities as well as their occupational performance and satisfaction with their occupational performance in each of the 2 women with MS. This preliminary intervention program should be further tested using randomized controlled trials.

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)0.001
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 score0.028
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.079
GPT teacher head0.390
Teacher spread0.310 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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