Neurocysticercosis in Bhutan: a cross-sectional study in people with epilepsy
Bibliographic record
Abstract
BACKGROUND: We sought to provide an assessment of the burden of neurocysticercosis among people with epilepsy (PWE) in Bhutan and evaluate the yield of various tests for Taenia solium. METHODS: PWE were enrolled at the National Referral Hospital in Thimphu (2014-2015). Serum was tested for anti-Taenia solium IgG using ELISA (Ab-ELISA), enzyme-linked immunoelectrotransfer blot (EITB), and parasite antigen. Results were compared to brain MRI. Participants were categorized as definite neurocysticercosis (MRI and EITB positive), probable neurocysticercosis (MRI or EITB positive), or without neurocysticercosis. Logistic regression models were constructed to explore clinicodemographic associations. RESULTS: There were 12/205 (6%, 95% CI 2%, 9%) definite and 40/205 (20%, 95% CI 14%, 25%) probable neurocysticercosis cases. 25/205 (12%) with positive EITB did not have neurocysticercosis on MRI, and 15/205 (7%) participants with positive MRI had negative EITB. Participants with neurocysticercosis-suggestive lesions on MRI had an average of 1.2 cysts (parenchymal 26/27; nodular/calcified stage 21/27). In a multivariable analysis, present age (OR 1.05, 95% CI 1.01,1.09, p=0.025) was positively associated with (combined probable or definite) neurocysticercosis while mesial temporal sclerosis on MRI (OR 0.294, 95% CI 0.144, 0.598, p=0.001) was negatively associated. CONCLUSIONS: Neurocysticercosis was associated with 6-25% of epilepsy in a Bhutanese cohort. Combining EITB and MRI would aid the diagnosis of neurocysticercosis among PWE since no test identified all cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".