The effect of transcranial direct current stimulation on cognitive performance in youth with persistent cognitive symptoms following concussion: a controlled pilot study
Bibliographic record
Abstract
OBJECTIVE: Explore the feasibility, tolerability, and early efficacy of transcranial direct current stimulation (tDCS) as a therapeutic intervention for youth with cognitive persistent post-concussion symptoms (PPCS). HYPOTHESIS: tDCS improves performance on a dual task working memory (WM) paradigm in youth with cognitive PPCS. PARTICIPANTS: Twelve youth experiencing cognitive PPCS. DESIGN: A quasi-randomized pilot trial was used to explore the tolerability of, and performance differences on, a dual N-Back WM task paired with active or sham tDCS over 3 sessions. MEASURES: Accuracy and reaction time on WM task and self-report of tDCS tolerability. RESULTS: Trends toward increases in accuracy from Day 1 to 3 seen in both groups. Active tDCS group performed better than sham on Day 2 in N-Back level N2 (p = .019), and marginally better than the sham group on Day 3 in level N3 (p = .26). Participants reported tDCS as tolerable; compared to the active tDCS group, the sham group reported more "considerable" (p = .078) and "strong" symptoms (p = .097). CONCLUSION: tDCS is a promising tool for enhancing WM performance and is a feasible and tolerable adjunct to behavioral interventions in youth with cognitive PPCS. A clinical trial to demonstrate efficacy is warranted.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".