The safety and feasibility of transcranial direct current stimulation combined with conservative treatment for patients with cervicogenic headaches: A double-blinded randomized control study protocol
Bibliographic record
Abstract
Background: Cervicogenic headaches (CGH) are common following concussion and whiplash injuries and significantly reduce patient quality of life. Conservative therapies such as ET (ET) and physiotherapy combined with injection-based therapies are cornerstones of treatment for CGH but have shown limited efficacy. Transcranial direct current stimulation (tDCS) over the primary motor cortex (M1) has shown promise in treating other chronic pain conditions. The primary aim of this trial is to evaluate the feasibility and safety of tDCS when combined with ET for the treatment of CGH. Methods: Adults (aged 18-65), blinded to treatment arm, will be randomized into one of two groups: active tDCS followed by ET or sham tDCS followed by ET. Transcranial direct current stimulation will be applied over M1 three times per week for 6-weeks and ET will be performed daily. The primary outcomes of this trial will be the feasibility and safety of the intervention. Feasibility will be defined as greater than 30 % recruitment, 70 % protocol adherence, and 80 % retention rate. Safety will be defined as no severe adverse events. Secondary exploratory outcomes will assess improvement in pain, strength, function, and quality of life. Conclusions: This trial aims to demonstrate the safety and feasibility of tDCS in combination with ET for the treatment of CGH. Cervicogenic headaches can be difficult to treat contributing to significant impairments function and quality of life. Transcranial direct current stimulation is a potential novel treatment to improve health outcomes in these patients. Registration: ClinicalTrials.gov-NCT05582616.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".