Feasibility of remotely supervised transcranial direct current stimulation and cognitive remediation: A systematic review
Bibliographic record
Abstract
With technological advancements and an aging population, there is growing interest in delivering interventions at home. Transcranial Direct Current Stimulation (tDCS) and Cognitive Remediation (CR) as well as Cognitive Training (CT) have been widely studied, but mainly in laboratories or hospitals. Thus, the objectives of this review are to examine feasibility and the interventions components to support the domiciliary administration of tDCS and CR. We performed a systematic search of electronic databases, websites and reference lists of included articles from the first date available until October 31, 2018. Articles included had to meet the following criteria: original work published in English using human subjects, majority of tDCS or CR intervention administered remotely. A total of 39 studies were identified (16 tDCS, 23 CR/CT, 5 using both tDCS & CT). Four studies were single case studies and two were multiple case studies. The remaining 33 studies had a range of 9-135 participants. Five tDCS and nine CR/CT studies were double blind randomized controlled trials. Most studies focused on schizophrenia (8/39) and multiple sclerosis (8/39). Literature examined suggests the feasibility of delivering tDCS or CR/CT remotely with the support of information and communication technologies.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".