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Record W3037684639 · doi:10.1177/1352458520930371

A novel in-home digital treatment to improve processing speed in people with multiple sclerosis: A pilot study

2020· article· en· W3037684639 on OpenAlexaff
Riley Bove, William Rowles, Chao Zhao, Annika Anderson, Samuel Friedman, Dawn Langdon, Amber Alexander, Simone Sacco, Roland G. Henry, Adam Gazzaley, Anthony Feinstein, Joaquin A. Anguera

Bibliographic record

VenueMultiple Sclerosis Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsRandomized controlled trialMedicineMultiple sclerosisClinical trialPhysical therapyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Objective: To assess whether a videogame-like digital treatment is superior to a control in improving processing speed in adults with multiple sclerosis (MS). Methods: Adults with MS and baseline Symbol Digit Modalities Test (SDMT) z-scores between −2 and 0 were enrolled in a double-blind randomized controlled clinical trial. After completing a baseline in-clinic evaluation (Visit 1), they were randomized to complete an in-home, tablet-based videogame-like digital treatment (AKL-T03) or control word game (AKL-T09) for up to 25 minutes/day, 5 days/week, for 6 weeks. A repeat in-clinic evaluation occurred at 6 weeks (Visit 2), and again 8 weeks later to determine persistence of effects (Visit 3). The pre-specified primary outcome was change in SDMT score between Visits 1 and 2. Results: SDMT increased at Visit 2 for participants randomized to both AKL-T03 ( p < 0.001) and AKL-T09 ( p = 0.024). These respective mean improvements were +6.10 and +3.55 (comparison p = 0.21). At Visit 3, 70% of participants randomized to AKL-T03 maintained a clinically meaningful 4+-point increase in SDMT above their baseline, compared with 37% for AKL-T09 ( p = 0.038). Conclusion: This in-home digital intervention resulted in substantial and durable improvements in processing speed. A larger randomized controlled clinical trial is planned. Trial Registration: This trial is registered on ClinicalTrials.gov under “NCT03569618,” https://clinicaltrials.gov/ct2/show/NCT03569618 .

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.145
GPT teacher head0.303
Teacher spread0.158 · 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.

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

Citations36
Published2020
Admission routes1
Has abstractyes

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