Diagnóstico de neurocisticercosis en pacientes con epilepsia residentes en el suroeste de la República Dominicana
Bibliographic record
Abstract
INTRODUCTION: Neurocysticercosis (NCC), a possible cause of epilepsy with limited epidemiological data in the Dominican Republic, is endemic in four provinces in the country's south-western region. This study aimed to determine the association between NCC and epilepsy among people living in these endemic regions, and to obtain preliminary data on the prevalence of NCC in these provinces. PATIENTS AND METHODS: A case-control design was used, consisting of 111 patients with epilepsy with unknown causes, and 60 controls without epilepsy or NCC. The diagnosis of NCC was based on computed tomography and magnetic resonance imaging of the skull, as well as Western immunoblotting for serum antibodies using Taenia solium, following the criteria of Del Brutto et al. RESULTS. NCC was found in 27% of the epileptic patients (n = 30/111) and in 5% of the controls (n = 3/60); the probability of the epileptic patients having NCC was seven times higher than the controls (odds ratio = 7.04, 95% confidence interval: 2.04-24.18; p < 0.001). The participants' sociodemographic characteristics, including their age, sex, level of education, occupation, and province of residence presented no statistical significance in terms of their association with NCC. CONCLUSIONS: This study suggests that NCC is strongly associated with epilepsy in the south-western region of the Dominican Republic, and highlights the need for public health measures to improve the prevention, diagnosis and treatment of both diseases.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".