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Record W2053748149 · doi:10.5014/ajot.2012.003590

Behavioral, Neurophysiological, and Descriptive Changes After Occupation-Based Intervention

2012· article· en· W2053748149 on OpenAlexaboutno aff
Camille Skubik-Peplaski, Cheryl Carrico, Laurel Nichols, Kenneth C. Chelette, Lumy Sawaki

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2012
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsTranscranial magnetic stimulationNeuroplasticityIntervention (counseling)Physical medicine and rehabilitationOccupational therapyStroke (engine)RehabilitationPsychologyPhysical therapyPsychological interventionMedicineDescriptive statisticsStimulationPsychiatryNeuroscience

Abstract

fetched live from OpenAlex

OBJECTIVE: We evaluated the effects of occupation-based intervention on poststroke upper-extremity (UE) motor recovery, neuroplastic change, and occupational performance in 1 research participant. METHOD: A 55-yr-old man with chronic stroke and moderately impaired UE motor function participated in 15 sessions of occupation-based intervention in a hospital setting designed to simulate a home environment. We tested behavioral motor function (Fugl-Meyer Assessment, Stroke Impact Scale, Canadian Occupational Performance Measure) and neuroplasticity (transcranial magnetic stimulation [TMS]) at baseline and at completion of intervention. We collected descriptive data on occupational participation throughout the study. RESULTS: All behavioral outcomes indicated clinically relevant improvement. TMS revealed bihemispheric corticomotor reorganization. Descriptive data revealed enhanced occupational performance. CONCLUSION: Occupation-based intervention delivered in a hospital-based, homelike environment can lead to poststroke neuroplastic change, increased functional use of the affected UE, and improved occupational performance.

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 score0.123
Threshold uncertainty score0.284

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 head0.068
GPT teacher head0.375
Teacher spread0.306 · 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

Citations20
Published2012
Admission routes1
Has abstractyes

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