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Neurologic Music Therapy in Sensorimotor Rehabilitation

2019· reference-entry· en· W3146309159 on OpenAlexaff
Corene P. Thaut, Klaus Stephan

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsCanadian University Music SocietyUniversity of Toronto
Fundersnot available
KeywordsPhysical medicine and rehabilitationMovement disordersRehabilitationPsychologyCerebral palsyMusic therapyCognitionAutismMultiple sclerosisSet (abstract data type)NeuroscienceNeurorehabilitationPerceptionMotor functionStroke (engine)Music perceptionMedicineDiseaseDevelopmental psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Since the 1990s, a strong body of research evidence has set the foundation for the use of rhythm and music as important tools in the development, rehabilitation, and maintenance of sensorimotor function, particularly in the treatment of neurologic disorders. This chapter examines the connection between music and sensorimotor function, and the underlying neurological principles and mechanisms behind music perception, production, and cognition as they relate to motor function. The role of neurologic music therapy to facilitate functional movement is discussed with a variety of populations and movement disorders including: Parkinson’s disease, stroke, traumatic brain injury, multiple sclerosis, cerebral palsy, autism and healthy elderly. The chapter is divided into sections related to acquired movement disorders, degenerative diseases, and developmental disorders.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.005

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 head0.021
GPT teacher head0.226
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations8
Published2019
Admission routes1
Has abstractyes

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