Characteristic Variation of Electromechanical Delay After the Botulinum Toxin Injection in Spastic Biceps Brachii Muscles
Bibliographic record
Abstract
The objective of this study was to characterize the effects of intramuscular botulinum toxin (BT) injections on the electromechanical delay (EMD) in spastic human biceps muscles. The EMD is calculated as the time lag between the muscle activation onset, as recorded from the surface electromyogram (sEMG), and the onset of recorded force. In a cohort of chronic stroke survivors, we compared the computed EMD derived from the spastic (injected) biceps brachii with that from the contralateral muscle. Eight participants were tested before and up to 3 months after a BT injection. At each session, participants followed an isometric trapezoidal force trajectory at 50 and 30%, respectively, of the tested maximum voluntary contraction (MVC). Joint force and sEMG signals were recorded as well. The number of zero crossings (ZC) of the sEMG during the steady-state portion of the task was also computed. The EMD post-BT was found to increase by 64 ± 10% (at 50% MVC) and 93 ± 18% (at 30% MVC) when compared to pre-BT values, while the number of sEMG-ZC, the mean MVC values, and the force-EMD slope exhibited striking reductions. These parameters, calculated on the contralateral side, remained relatively constant across sessions, with the EMD significantly lower and the MVC values much higher. We discuss potential contributing factors to an increase in EMD values on the affected side, both pre- and post-BT. The observed co-variation across sessions of the increased EMD values with the decreased ZC estimates, a surrogate of motor outflow, and, potentially, more compliant muscle fascicles suggests that the altered motor unit (MU) behavior contributes, at least in part, to the delayed force production.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".