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Record W2179018982 · doi:10.1123/mcj.17.3.283

Neural and Muscular Determinants of Dorsiflexor Weakness in Chronic Stroke Survivors

2013· article· en· W2179018982 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMotor Control · 2013
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCoactivationPhysical medicine and rehabilitationStroke (engine)ElectromyographyWeaknessMedicineMuscle weaknessCardiologyPhysical therapyInternal medicineAnatomyPhysics

Abstract

fetched live from OpenAlex

Few examined the contribution of neural and muscular deficits to weakness in the same stroke subject. We determined maximal voluntary contraction (MVC) and 50 Hz torques, activation (twitch interpolation), electromyographic (EMG) amplitude and antagonist coactivation, and muscle volume using magnetic resonance imaging (MRI) of the dorsiflexors bilaterally in 7 chronic stroke subjects (40-67 y). Recordings of MVC and 50 Hz torque were also done in 7 control subjects (24-69 y) without stroke. The MVC torque was smaller in the contralesional than ipsilesilesional limb (29.8 ± 21.3 Nm vs. 42.5 ± 12.0 Nm, p = .04), and was associated with deficits in activation (r2 = .77) and EMG amplitude (r2 = .71). Antagonist coactivation percentage was not significantly different between limbs. Muscle volume, 50 Hz torque, and specific torque (50Hz torque/muscle volume) were also not different between sides. The concept that atrophy is commonplace after stroke is not supported by the results. Our findings indicate that dorsiflexor weakness in mobile stroke survivors is not explained by atrophy or reduced torque generating capacity suggesting an important role for central factors.

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.

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 score0.581
Threshold uncertainty score0.396

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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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