A Scoping Review on the Use of Non-Invasive Brain Stimulation Techniques for Persistent Post-Concussive Symptoms
Bibliographic record
Abstract
BACKGROUND: In the context of managing persistent post-concussive symptoms (PPCS), existing treatments like pharmacotherapy, cognitive behavioral therapy, and physical rehabilitation show only moderate effectiveness. The emergence of neuromodulation techniques in PPCS management has led to debates regarding optimal stimulation parameters and their overall efficacy. METHODS: this scoping review involved a comprehensive search of PubMed and ScienceDirect databases, focusing on controlled studies examining the therapeutic potential of non-invasive brain stimulation (NIBS) techniques in adults with PPCS. RESULTS: Among the 940 abstracts screened, only five studies, encompassing 103 patients (12 to 29 per study), met the inclusion criteria. These studies assessed the efficacy of transcranial direct current stimulation (tDCS), or repetitive transcranial magnetic stimulation (rTMS), applied to specific brain regions (i.e., the left dorsolateral pre-frontal cortex (DLPFC) or left motor cortex (M1)) for addressing cognitive and psychological symptoms, headaches, and general PPCSs. The results indicated improvements in cognitive functions with tDCS. In contrast, reductions in headache intensity and depression scores were observed with rTMS, while no significant findings were noted for general symptoms with rTMS. CONCLUSION: although these pilot studies suggest promise for rTMS and tDCS in PPCS management, further research with larger-scale investigations and standardized protocols is imperative to enhance treatment outcomes for PPCS patients.
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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".