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Record W4406676525 · doi:10.2340/jrm-cc.v8.42152

Combining transcranial direct current stimulation and robotic-assisted training to address upper extremity deficits in acute disseminated encephalomyelitis: a case report

2025· article· en· W4406676525 on OpenAlexaff
Maureen Ahiatsi, Matthieu Vincenot, Christian Bocti, Guillaume Léonard, Marie-Hélène Milot

Bibliographic record

VenueJournal of Rehabilitation Medicine – Clinical Communications · 2025
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTranscranial direct-current stimulationTranscranial magnetic stimulationPhysical medicine and rehabilitationAcute disseminated encephalomyelitisMedicineNeuroscienceDeep transcranial magnetic stimulationPsychologyStimulationCentral nervous system

Abstract

fetched live from OpenAlex

A 45-year-old woman with persistent acute disseminated encephalomyelitis sequelae participated in a 4-week robotic-assisted training program of her affected arm combined with transcranial direct current stimulation. Clinical indicators such as range of motion, motor function of the affected arm, fatigue, pain, spasticity, and quality of life were assessed pre/post-intervention. The results demonstrated clinical benefits post- intervention, with an improvement in range of motion and affected arm motor function, fatigue, and quality of life of the patient. Although preliminary, the results of this case report support the development of innovative technologically assisted rehabilitative strategies for individuals with acute disseminated encephalomyelitis sequelae, including a robot-assisted rehabilitation program coupled with neurostimulation sessions. Further large-scale randomized controlled trials are needed to confirm these findings and rigorously assess the efficacy of this approach in acute disseminated encephalomyelitis individuals.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.055
GPT teacher head0.394
Teacher spread0.339 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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