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Record W2051510644 · doi:10.5014/ajot.2011.002063

Modified Constraint-Induced Movement Therapy for Elderly Clients With Subacute Stroke

2011· article· en· W2051510644 on OpenAlexafffundabout
Martha S. McCall, Sara McEwen, Angela Colantonio, David L. Streiner, Deirdre Dawson

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2011
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsSt. John's Rehab HospitalBaycrest HospitalMcMaster UniversityToronto Rehabilitation InstituteUniversity of Toronto
FundersNational Institutes of HealthToronto Rehabilitation InstituteOntario Ministry of Health and Long-Term CareHeart and Stroke Foundation of Canada
KeywordsConstraint-induced movement therapyStroke (engine)RehabilitationMedicineOccupational therapyPhysical therapyPhysical medicine and rehabilitationRandomized controlled trialIntervention (counseling)PopulationGerontologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

A growing body of research, including evidence from numerous randomized controlled trials, suggests that constraint-induced movement therapy (CIMT) reduces impairment. The mean age of participants in most studies has been < 65 yr, even though most stroke survivors are older than that. We investigated the efficacy of a modified CIMT protocol on participation, activity, and impairment in a population of older adults experiencing subacute stroke. Using an interrupted time series design, 4 older adults (mean age = 82) were assessed before and after intervention. Although none of the participants adhered to the 6-hr per day self-practice aspect of the CIMT protocol, considerable improvements were noted in participation, as measured using the Canadian Occupational Performance Measure. Some improvements were also noted at the level of impairment and activity. This work accords with previous literature on CIMT and has important implications for the evolution of stroke rehabilitation in elderly people.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.447

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 head0.083
GPT teacher head0.346
Teacher spread0.263 · 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

Citations25
Published2011
Admission routes3
Has abstractyes

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