Contributions of voluntary activation deficits to hand weakness after stroke
Bibliographic record
Abstract
BACKGROUND: Hemiparetic stroke survivors often exhibit profound weakness in the digits of the paretic hand, but the relative contribution of potential biomechanical and neurological impairment mechanisms is not known. Establishing sources of impairment would help in guiding treatment. OBJECTIVE: The present study sought to quantify the role of diminished capacity to voluntarily active finger flexor and extensor muscles as one possible neurological mechanism. METHODS: Two groups of stroke survivors with "severe" (N = 9) or "moderate" (N = 9) hand impairment and one group of neurologically intact individuals (N = 9) participated. Subjects were asked to create isometric flexion force and extension force, respectively, with the tip of the middle finger. The maximum voluntary force (MVF) and the maximum stimulated force (MSF) produced by an applied train of electrical current pulses (MSF) were recorded for flexion and extension. Percent voluntary activation (PVA) was computed from MVF and MSF. RESULTS: Significant deficits in both MVF and PVA were observed for stroke subjects compared to control subjects. For example, activation deficits were >80% for extensor digitorum communis (EDC) for the "severe" group. Maximum voluntary force and PVA deficits were greater for EDC than for flexor digitorum superficialis (FDS) for stroke subjects with severe impairment. Maximum voluntary force and PVA correlated significantly for stroke subjects but not for control subjects. CONCLUSIONS: Although extrinsic finger muscles could be successfully recruited electrically, voluntary excitation of these muscles was substantially limited in stroke survivors. Thus, finger weakness after stroke results predominantly from the inability to fully activate the muscle voluntarily.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".