Transcranial direct current stimulation to enhance motor function in spinal cord injury: Pilot data
Bibliographic record
Abstract
Several lines of evidence indicate that a non-invasive form of brain stimulation called transcranial direct current stimulation (tDCS) can facilitate motor recovery after stroke. However, there is no available data about how tDCS may enhance outcomes of intensive, task-oriented upper extremity (UE) motor training in people with spinal cord injury (SCI). Moreover, there is a lack of effective interventions to enhance recovery of UE motor function after SCI, especially in chronic cases. Thus, we are conducting a double-blind, randomized, controlled study of how tDCS paired with intensive task-oriented training affects UE motor function in subjects with motor incomplete cervical SCI. Our central hypothesis is that subjects who receive anodal tDCS paired with intensive task-oriented training 3 days a week for 8 weeks will have significantly more improved UE motor performance than controls receiving sham tDCS paired with identical training. Furthermore, motor improvement will correlate with corticospinal reorganization (motor maps) measured by transcranial magnetic stimulation (TMS). Outcome measures for motor performance include Spinal Cord Independence Measure-Ill, Canadian Occupational Performance Measure, and Medical Research Council scale administered at baseline, at midpoint, and immediately post-intervention. Here, we present our preliminary results (n=2) of this ongoing study.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".