Remote Electroencephalography Monitoring of Epilepsy in Adults: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Electroencephalography (EEG) monitoring is a key tool in diagnosing and determining treatment for people with epilepsy; however, obtaining sufficient high-quality data can be a time-consuming, costly, and inconvenient process for patients and health care providers. Remote EEG monitoring has the potential to improve patient experience, data quality, and accessibility for people with intellectual or developmental disabilities. OBJECTIVE: The purpose of this scoping review is to provide an overview of the current research evidence and knowledge gaps regarding the use of remote EEG monitoring interventions for adults with epilepsy. METHODS: The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) and Population, Intervention, Comparator, Outcome, and Study (PICOS) frameworks will be used to structure the review. Searches will be conducted in 6 databases (PubMed, MEDLINE, Embase, CINAHL, Web of Science, and ClinicalTrials.gov) for articles published in English that evaluate at least one out-of-hospital EEG monitoring intervention or device for adults with epilepsy. A descriptive analysis will be conducted to summarize the results; key themes and gaps in the literature will be discussed. RESULTS: Results will be included in the scoping review, which will be submitted for publication by April 2022. CONCLUSIONS: This scoping review will summarize the state of the field of remote EEG monitoring interventions for adults with epilepsy and provide an overview of the strengths, weaknesses, and gaps in the research. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/33812.
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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 | 0.070 | 0.069 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.096 | 0.015 |
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".