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Record W1979559238 · doi:10.1080/09593980500321127

A modified constraint-induced therapy (mCIT) program for the upper extremity of a person with chronic stroke

2005· article· en· W1979559238 on OpenAlexfundno aff
Renée M. Hakim, Stephen Kelly, Marybeth Grant-Beuttler, Brian C. Healy, Jesse Krempasky, Sean Moore

Bibliographic record

VenuePhysiotherapy Theory and Practice · 2005
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
FundersHeart and Stroke Foundation of Canada
KeywordsMedicinePhysical therapyStroke (engine)Physical medicine and rehabilitationChronic strokeConstraint-induced movement therapyOccupational therapyUpper limbRehabilitation

Abstract

fetched live from OpenAlex

The purpose of this case report was to assess the effect of a reduced intensity protocol for daily modified constraint-induced therapy (mCIT) without use of a restraint on the function of the upper extremity (UE) in an individual with a chronic stroke. A 57 year-old patient one year following a stroke participated in a two-hour mCITprogram for ten weekdays over a period of two weeks. During this period, voluntary use of the involved extremity was encouraged for 90% of waking hours at home without use of a restraint. Examination was conducted before and after intervention, and at a one month follow-up visit. Outcome measures included: the Wolf Motor Function Test (WMFT), the Motor Activity Log (MAL), and motion analysis of a reach and grasp task using Charnwood's CODA mpx 30. The patient improved or remained the same in functional upper extremity tasks on both the WMFT and MAL. The data from the motion analysis showed that grasp time and maximum pinch angle improved for bilateral UEs. Reaching profile graphed over time had fewer changes in velocity and fewer periods of acceleration and deceleration post-intervention. This reduced intensity program shows promise as an effective, feasible, and patient-preferred application of mCIT in a clinical setting.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.030
GPT teacher head0.353
Teacher spread0.324 · 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 designOther design
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

Citations14
Published2005
Admission routes1
Has abstractyes

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