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Effect of Force-Feedback Treatments in Patients with Chronic Motor Deficits After a Stroke

2002· article· en· W2090970826 on OpenAlexaff
Daniel Bourbonnais, Suzie Bilodeau, Yves Lepage, Nicole Beaudoin, Denis Gravel, Robert Forget

Bibliographic record

VenueAmerican Journal of Physical Medicine & Rehabilitation · 2002
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsQuebec Rehabilitation Research NetworkUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsUpper limbLower limbMedicinePhysical medicine and rehabilitationStroke (engine)Chronic strokeRandomized controlled trialPhysical therapyGaitRehabilitationSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the effects a motor reeducation approach based on static dynamometers used to provide feedback on the force produced. DESIGN: The study design was a single-blind, randomized, controlled trial. Chronic stroke subjects participated in a 6-wk, thrice-weekly, force-feedback program of either the upper paretic limb (n = 13) or the lower paretic limb (n = 12). Baseline and postintervention assessments of the performance of both the upper and the lower limb were measured for each subject, the untreated paretic limb of each group serving as a control for the other group. RESULTS: With the exception of the handgrip force, strength measurements of the treated limb increased after completion of the treatment. The outcome measurements of the upper limb of the subjects included in the upper paretic limb were not significantly different after treatment from those measured in the lower paretic limb. In contrast, gait velocity and the distance walked in 2 min increased after treatment in the lower paretic limb as compared with the upper paretic limb, whereas the scores in the Fugl-Meyer test for the lower limb and the timed up-and-go test did not increase for either group after treatment. CONCLUSION: The results indicate that treatment of the lower limb based on force feedback produces an improvement of gait velocity.

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)0.001
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 score0.385
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.003
GPT teacher head0.243
Teacher spread0.240 · 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

Citations94
Published2002
Admission routes1
Has abstractyes

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