Integrating Transcranial Direct Current Stimulation into an Existing Inpatient Physiotherapy Program to Enhance Motor Learning in an Adolescent with Traumatic Brain Injury: A Case Report
Bibliographic record
Abstract
PURPOSE: Describe how transcranial direct current stimulation (tDCS) was incorporated into an inpatient physiotherapy program for an adolescent with severe traumatic brain injury (TBI), detail the motor learning focus of the physiotherapy sessions, and summarize gross motor progress. METHOD: This case report describes an adolescent who received 20 minutes of anodal tDCS immediately prior to 16 physiotherapy sessions over four weeks. Potential side effects were tracked pre/post tDCS. Gross motor outcomes were measured pre-intervention, post-intervention, and three months post-intervention. Physiotherapy session content was analyzed using therapist documentation and the Motor Learning Strategies Rating Instrument. RESULTS: The youth tolerated tDCS well. The primary side effect was itchiness under the electrodes during tDCS sessions. His mobility progressed from wheelchair use pre- 'tDCS + physiotherapy' to ambulation with a walker post-intervention. His Gross Motor Function Measure score increased 33.1% points pre/post intervention. Session tasks often had several foci (e.g., skill acquisition, strength, and balance) with task focus changing as the youth progressed. Various motor learning strategies were layered within tasks to support performance and learning. CONCLUSIONS: tDCS was successfully integrated into an existing inpatient physiotherapy program for an adolescent with TBI. This protocol provides a structure for implementing, monitoring, and measuring tDCS + physiotherapy in pediatric rehabilitation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".