Tablet‐based electroencephalography diagnostics for patients with epilepsy in the West African Republic of Guinea
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Epilepsy is most common in lower-income settings where access to electroencephalography (EEG) is generally poor. A low-cost tablet-based EEG device may be valuable, but the quality and reproducibility of the EEG output are not established. METHODS: Tablet-based EEG was deployed in a heterogeneous epilepsy cohort in the Republic of Guinea (2018-2019), consisting of a tablet wirelessly connected to a 14-electrode cap. Participants underwent EEG twice (EEG1 and EEG2), separated by a variable time interval. Recordings were scored remotely by experts in clinical neurophysiology as to data quality and clinical utility. RESULTS: There were 149 participants (41% female; median age 17.9 years; 66.6% ≤21 years of age; mean seizures per month 5.7 ± SD 15.5). The mean duration of EEG1 was 53 ± 12.3 min and that of EEG2 was 29.6 ± 12.8 min. The mean quality scores of EEG1 and EEG2 were 6.4 [range, 1 (low) to 10 (high); both medians 7.0]. A total of 44 (29.5%) participants had epileptiform discharges (EDs) at EEG1 and 25 (16.8%) had EDs at EEG2. EDs were focal/multifocal (rather than generalized) in 70.1% of EEG1 and 72.5% of EEG2 interpretations. A total of 39 (26.2%) were recommended for neuroimaging after EEG1 and 22 (14.8%) after EEG2. Of participants without EDs at EEG1 (n = 53, 55.8%), seven (13.2%) had EDs at EEG2. Of participants with detectable EDs on EEG1 (n = 23, 24.2%), 12 (52.1%) did not have EDs at EEG2. CONCLUSIONS: Tablet-based EEG had a reproducible quality level on repeat testing and was useful for the detection of EDs. The incremental yield of a second EEG in this setting was ~13%. The need for neuroimaging access was evident.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".