The Yield of EEG Studies in a Lower Income Country Epilepsy Referral Center (P1.273)
Bibliographic record
Abstract
Objective: To assess the occurrence and predictors of epileptiform EEG abnormalities in people with epilepsy at a national referral clinic in a lower income country without neurologists or routine neurological services. Background: Low income and low-middle income countries - such as Bhutan - carry a heavy burden of epilepsy, but the role of EEGs in these settings is not well characterized. Methods: Participants of any age who had seizures or suspected seizures were recruited on a “first come, first served” basis at the Jigme Dorji Wangchuck National Referral Hospital in Thimphu, Bhutan through physician, health care worker or traditional healer referrals. Participants completed questionnaires in English or verbal translations to Dzongkha and underwent 25-minute EEG studies performed by a registered EEG technician. EEGs were interpreted by at least two members of a panel of 8 independent board-certified neurologists in the United States or Canada. Conflicts were adjudicated by additional members when required. Results: 267 participants (n=77 <18 years) were recruited over 9 months in 2014-2015. Of these participants, 167 (63[percnt]) had EEGs that were normal, 35 (13[percnt]) showed slowing but no epileptiform activity, and 65 (24[percnt]) showed epileptiform activity with or without background slowing (40 asymmetric epileptiform activity, 11 generalized spike-wave activity, 12 bilateral or other epileptiform activity, 2 acute seizures). Unintentional seizure-related injury (odds ratio 2.2, 95[percnt] confidence interval 1.2, 4.2; p=0.015) and history of head injury (OR 2.8, 95[percnt] CI 1.4, 5.6; p<0.001) were significantly associated with epileptiform EEGs, but loss of consciousness, age of epilepsy diagnosis and use of multiple AEDs were not. Conclusions: EEG abnormalities may be used to guide treatment decisions for people with epilepsy in this lower income country. Epileptiform EEGs were significantly associated with self-reported seizure-related injury. These results support the diagnostic value of EEG in resource-limited settings.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".