Application of electrophysiological measures in spinal cord injury clinical trials: a narrative review
Bibliographic record
Abstract
STUDY DESIGN: Narrative review. OBJECTIVES: To discuss how electrophysiology may contribute to future clinical trials in spinal cord injury (SCI) in terms of: (1) improvement of SCI diagnosis, patient stratification and determination of exclusion criteria; (2) the assessment of adverse events; and (3) detection of therapeutic effects following an intervention. METHODS: An international expert panel for electrophysiological measures in SCI searched and discussed the literature focused on the topic. RESULTS: Electrophysiology represents a valid method to detect, track, and quantify readouts of nerve functions including signal conduction, e.g., evoked potentials testing long spinal tracts, and neural processing, e.g., reflex testing. Furthermore, electrophysiological measures can predict functional outcomes and thereby guide rehabilitation programs and therapeutic interventions for clinical studies. CONCLUSION: Objective and quantitative measures of sensory, motor, and autonomic function based on electrophysiological techniques are promising tools to inform and improve future SCI trials. Complementing clinical outcome measures, electrophysiological recordings can improve the SCI diagnosis and patient stratification, as well as the detection of both beneficial and adverse events. Specifically composed electrophysiological measures can be used to characterize the topography and completeness of SCI and reveal neuronal integrity below the lesion, a prerequisite for the success of any interventional trial. Further validation of electrophysiological tools with regard to their validity, reliability, and sensitivity are needed in order to become routinely applied in clinical SCI trials.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".