Yield of Brain MRI in Clinically Diagnosed Epilepsy in the Kingdom of Bhutan: A Prospective Study
Bibliographic record
Abstract
BACKGROUND: People with epilepsy (PWE) in low- and middle-income countries may not access the health resources that are considered optimal for epilepsy diagnosis. The diagnostic yield of magnetic resonance imaging (MRI) has not been well studied in these settings. OBJECTIVES: To report the diagnostic yield of brain MRI and identify clinical associations of abnormal MRI findings among PWE in a neurocysticercosis-endemic, resource-limited setting and to identify the proportion and putative structural brain causes of drug-resistant epilepsy. METHODS: PWE were prospectively enrolled at the Jigme Dorji Wangchuck National Referral Hospital in Bhutan (2014-2015). Each participant completed clinical questionnaires and a 1.5-Tesla brain MRI. Each MRI was reviewed by at least 1 radiologist and neurologist in Bhutan and the United States. A working definition of drug-resistant epilepsy for resource-limited settings was given as (a) seizures for >1 year, (b) at least 1 seizure in the prior year, and (c) presently taking 2 or more antiepileptic drugs (AEDs). Logistic regression models were constructed to test the cross-sectional association of an abnormal brain MRI with clinical variables. FINDINGS: A total of 217 participants (125 [57%] female; 54 [25%] < 18 years old; 199 [92%] taking AEDs; 154 [71%] with a seizure in the prior year) were enrolled. There was a high prevalence of abnormal brain MRIs (176/217, 81%). Mesial temporal sclerosis was the most common finding (n = 115, 53%, including 24 children), exceeding the number of PWE with neurocysticercosis (n = 26, 12%, including 1 child) and congenital/perinatal abnormalities (n = 29, 14%, including 14 children). The number of AEDs (odds ratio = .59, P = .03) and duration of epilepsy (odds ratio = 1.11, P = .02) were significantly associated with an abnormal MRI. Seizure in the prior month was associated with the presence of mesial temporal sclerosis (odds ratio = .47, P = .01). A total of 25 (12%) participants met our definition of drug-resistant epilepsy, with mesial temporal sclerosis (n = 10), congenital malformations (n = 5), and neurocysticercosis (n = 4) being the more common findings. CONCLUSIONS: The prevalence of abnormalities on brain MRI for PWE in resource-limited settings is high as a result of a diffuse range of etiologies, most commonly mesial temporal sclerosis. Drug-resistant epilepsy accounted for 12% of the referral population in a conservative estimation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".