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Record W2161907530 · doi:10.1093/ptj/85.5.413

Control of Lead and Trail Limbs During Obstacle Crossing Following Stroke

2005· article· en· W2161907530 on OpenAlexaff
Catherine M. Said, Patricia A. Goldie, Elsie Culham, W. A. Sparrow, Aftab E. Patla, Meg E. Morris

Bibliographic record

VenuePhysical Therapy · 2005
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of WaterlooQueen's University
Fundersnot available
KeywordsObstacleStroke (engine)Physical medicine and rehabilitationMedicineKinematicsPreferred walking speedPhysical therapyEngineeringPhysics

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Obstacle crossing is compromised following stroke. The purpose of this study was to quantify modifications during obstacle clearance following stroke. SUBJECTS: Twelve subjects with stroke and 12 subjects without stroke participated in the study. METHODS: Kinematic variables were measured while participants crossed a 4-cm-high obstacle. Subjects with stroke walked at a self-selected speed; subjects without stroke walked at a comparable speed and at a self-selected speed. RESULTS: Several modifications were observed following stroke with both groups walking at self-selected speeds. The affected lead limb was positioned closer to the obstacle before crossing. Affected trail-limb clearance over the obstacle was reduced. Both affected and unaffected lead and trail limbs landed closer to the obstacle after clearance. Swing time was increased in the affected lead limb after obstacle clearance. Fewer modifications were detected at matched walking speed; the trail limb still landed closer to the obstacle. DISCUSSION AND CONCLUSION: Modifications during obstacle crossing following stroke may be partly related to walking speed. The findings raise issues of safety because people with stroke demonstrated reduced clearance of a 4-cm obstacle and limb placement closer to the obstacle after clearance.

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 score0.959
Threshold uncertainty score0.387

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 head0.031
GPT teacher head0.366
Teacher spread0.335 · 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

Citations61
Published2005
Admission routes1
Has abstractyes

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