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Record W2560022147 · doi:10.1016/j.heliyon.2016.e00208

A novel bilateral lower extremity mirror therapy intervention for individuals with stroke

2016· article· en· W2560022147 on OpenAlexaff
Lucas D. Crosby, Stephanie Marrocco, Janet Brown, Kara K. Patterson

Bibliographic record

VenueHeliyon · 2016
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity Health NetworkToronto Rehabilitation InstituteUniversity of TorontoWestern University
Fundersnot available
KeywordsRehabilitationStroke (engine)Physical medicine and rehabilitationPhysical therapyGaitMedicinePsychological interventionIntervention (counseling)Motor learningPsychology

Abstract

fetched live from OpenAlex

Despite improvements made in stroke rehabilitation, motor impairment and gait deficits persist at discharge. New interventions are needed. Mirror therapy has promise as one element of a rehabilitation program. The primary objectives were to 1) describe a bilateral, lower extremity mirror therapy (LE-MT) device and training protocol and 2) investigate the feasibility of LE-MT. A LE-MT device was constructed to train bilateral LE movements for 30 min, 3 times/week for 4 weeks, as an adjunct to physiotherapy in three individuals post-stroke. Sessions were digitally recorded and reviewed to extract feasibility measures; repetitions, rests and session duration. Pre and post measures of gait and motor impairment were taken. Two participants completed 100% of the sessions and a third completed 83% due to a recurrence of pre-existing back pain. Repetitions increased and session duration was maintained. Number of rests decreased for two participants and increased for one participant. Participants reported fatigue and mild muscle soreness but also that the intervention was tolerable. Positive gait changes included increased velocity and decreased variability. LE motor impairment also improved. A bilateral LE-MT adjunct intervention for stroke is feasible and may have positive effects. A history of low back pain should be a precaution.

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)0.000
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 score0.316
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.029
GPT teacher head0.303
Teacher spread0.274 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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