Poster (Technology Innovation) ID 1984794
Bibliographic record
Abstract
Background Cervical spinal cord injury (SCI) can cause significant impairment and disability with an impact on individuals’ quality of life and independence. Surface electromyography (SEMG) is a sensitive and non-invasive technique to measure muscle activity and has demonstrated great potential in capturing the impact from SCI. The mechanisms of SCI damage on SEMG signal characteristics are multi-faceted and difficult to study in vivo. Objective Use validated computational models to characterize changes in SEMG signal after SCI and identify SEMG features that are sensitive and specific to the impact from different aspects of SCI. Method Starting from existing computational models for motor neuron pool organization and for motor unit action potential generation for healthy neuromuscular systems, we set up scenarios to model alterations in upper motor neurons, lower motor neurons, and the number of muscle fibers within each motor unit after SCI. After simulating SEMG signals from each scenario, we extracted time and frequency domain features and investigated the impact of SCI disruptions on SEMG features using the Pearson correlation between a feature and the extent of a given disruption. Findings Commonly used amplitude-based SEMG features cannot differentiate between injury scenarios. A broader set of features provides greater specificity to the type of damage present. Conclusion We demonstrated a novel approach to mechanistically relate SEMG features to different types of neuromuscular alterations after SCI. This work contributes to a deeper understanding and exploitation of SEMG in clinical applications, which will ultimately improve patient outcomes after SCI.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.781 | 0.603 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".