Repetitive transcranial magnetic stimulation for enhancing motor function after spinal cord injury: a narrative review
Bibliographic record
Abstract
Spinal cord injury (SCI) often leads to disabilities that significantly impact quality of life, highlighting the need for effective rehabilitation strategies. Repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising neuromodulatory approach to enhance neuronal plasticity and promote motor recovery following SCI. This narrative review examines the current state of evidence regarding the therapeutic use of rTMS for motor function recovery after SCI and outlines key methodological considerations to guide future research. To address these aims, we summarize various rTMS techniques and evaluate their overall efficacy in improving motor function in individuals with SCI. Among the fourteen studies reviewed, early rTMS protocols primarily utilized low-frequency stimulation, whereas more recent approaches have adopted higher frequencies and more complex patterned protocols. Despite considerable heterogeneity in stimulation parameters, most studies reported beneficial effects of rTMS, including reduction in spasticity and improvements in voluntary motor function of both upper and lower limbs. These findings demonstrate that rTMS holds promise as an effective tool for SCI rehabilitation, with limited to moderate evidence supporting reductions in spasticity, increased muscle strength, and enhanced functional outcomes. However, many of these findings are derived from small sample sizes, varied protocols, and studies lacking rigorous control conditions. The review emphasizes the need for standardized functional and electrophysiological assessments to systematically evaluate motor outcomes following rTMS interventions. Additionally, larger, well-controlled clinical trials incorporating consistent physical therapy protocols are essential to confirm the efficacy of rTMS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".