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Record W3197616235 · doi:10.7224/1537-2073.2020-007

Effectiveness of Three-Dimensional Multiple-Object Tracking in Patients with Multiple Sclerosis

2020· article· en· W3197616235 on OpenAlexaff
Sebastian Harenberg, Jennifer St.Onge, Jill Robinson, Omorowa Eguakun, Anthony Feinstein, Kim D. Dorsch, Rumit Singh Kakar, Raghig Abdulhakim, Zia ul Mustafa Rehman, Melad Shawush, Vinesh Pillay

Bibliographic record

VenueInternational Journal of MS Care · 2020
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsSaskatchewan HealthSaskatchewan Health AuthoritySt. Francis Xavier UniversityUniversity of ReginaUniversity of Toronto
Fundersnot available
KeywordsMedicineMultiple sclerosisCognitionPhysical medicine and rehabilitationStroop effectTrail Making TestNeuropsychologyRehabilitationCognitive rehabilitation therapyCognitive trainingPhysical therapyAudiologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Computerized cognitive training remains an attractive supplemental modality to enhance rehabilitation in multiple sclerosis (MS). The objective of the present study was to assess the usability of three-dimensional multiple-object tracking (3D-MOT) in patients with MS. METHODS: In this pilot study, 16 patients with relapsing-remitting MS and nine age-matched controls participated in four 30-minute training sessions of 3D-MOT. Computerized neuropsychological tests, including driving readiness (ie, Useful Field of View) and cognitive function (ie, Stroop Color and Word Test, Paced Visual Serial Addition Test, Symbol Digit Modalities Test) were conducted at baseline and at the conclusion of training. RESULTS: Although scoring lower in 3D-MOT, the MS group improved their 3D-MOT scores in similar magnitude as the control group. The 3D-MOT training led to significant improvements in driving readiness in the MS group. Taken together, 3D-MOT training showed similar effectiveness in patients with MS as in age-matched controls. CONCLUSIONS: Training with 3D-MOT may be an accessible and remotely administrable supplement to cognitive rehabilitation protocols for patients with MS.

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 score0.006
Threshold uncertainty score0.459

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 integrity0.0000.001
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 head0.048
GPT teacher head0.337
Teacher spread0.289 · 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
Published2020
Admission routes1
Has abstractyes

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