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Record W2161073680 · doi:10.3138/ptc.59.2.99

Effectiveness of a Modified Constraint-Induced Movement Therapy Regimen for Upper Limb Ability after Stroke: A Retrospective Case Series

2007· article· en· W2161073680 on OpenAlexvenueaboutno aff
Ted J Stevenson, Leyda Thalman

Bibliographic record

VenuePhysiotherapy Canada · 2007
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsRegimenConstraint-induced movement therapyMedicinePhysical therapyStroke (engine)Physical medicine and rehabilitationUpper limbRepeated measures designOccupational therapySurgery

Abstract

fetched live from OpenAlex

Purpose: To examine the responses of individuals living with the effects of stroke to the modified regimen of constraint-induced movement therapy (CIMT) provided at our facility. Method: The charts of 12 individuals who had completed our facility's modified CIMT regimen were reviewed. This regimen is composed of four hours per day of supervised training of upper limb activities for 10 consecutive weekdays concurrent with restraint of the less involved limb for a goal of 90 per cent of waking hours. Before beginning training, all participants were able to pick up and release a water glass. Pre-training, post-training and followup assessments were performed using the Canadian Occupational Performance Measure, a modified version of the Box and Block test and the 30-item Motor Activity Log. Results: Repeated measures analysis of variance revealed statistically significant improvements over the treatment period with maintenance of these improvements six months later. Clinically significant improvements were seen for 10 of the 12 participants after the training period. Eight of these 10 participants continued to show clinically significant improvements at the six-month assessment. Conclusions: This project provides preliminary evidence that this CIMT regimen, modified for routine clinical application, may be effective.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.285
Teacher spread0.275 · 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 designBench or experimental
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

Citations4
Published2007
Admission routes2
Has abstractyes

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