Utility of transcranial magnetic stimulation in the assessment of spinal cord injury: Current status and future directions
Bibliographic record
Abstract
Comprehensive assessment following traumatic spinal cord injury (SCI) is needed to improve prognostication, advance the understanding of the neurophysiology and better targeting of clinical interventions. The International Standards for Neurological Classification of Spinal Cord Injury is the most common clinical examination recommended for use after a SCI. In addition, there are over 30 clinical assessment tools spanning across different domains of the International Classification of Functioning, Disability, and Health that have been validated and recommended for use in SCI. Most of these tools are subjective in nature, have limited value in predicting neurologic recovery, and do not provide insights into neurophysiological mechanisms. Transcranial magnetic stimulation (TMS) is a non-invasive neurophysiology technique that can supplement the clinical assessment in the domain of body structure and function during acute and chronic stages of SCI. TMS offers a better insight into neurophysiology and help in better detection of residual corticomotor connectivity following SCI compared to clinical assessment alone. TMS-based motor evoked potential and silent period duration allow study of excitatory and inhibitory mechanisms following SCI. Changes in muscle representations in form of displacement of TMS-based motor map center of gravity or changes in the map area can capture neuroplastic changes resulting from SCI or following rehabilitation. Paired-pulse TMS measures help understand the compensatory reorganization of the cortical circuits following SCI. In combination with peripheral stimulation, TMS can be used to study central motor conduction time and modulation of spinal reflexes, which can be used for advanced diagnostic and treatment purposes. To strengthen the utility of TMS in SCI assessment, future studies will need to standardize the assessment protocols, address population-specific concerns, and establish the psychometric properties of TMS-based measurements in the SCI population.
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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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".