What proportion of cases of epilepsy are actually caused by neurocysticercosis?
Bibliographic record
Abstract
The recent study by Del Brutto et al.1 sought to determine the association between neurocysticercosis (NC) and epilepsy in an endemic rural population in Ecuador, explaining that their study addressed selection bias likely present in earlier studies, many of which have been scrutinized in recent reviews.2, 3 As the authors acknowledge, Prasad et al.4 also conducted a population-based study in India some years before, finding similar results, that is, that people with epilepsy had greater odds of also having NC. There is abundant information that all evolutive phases of the parasite in the brain, including calcification, are related to seizures.5 A genetic predisposition for low seizure threshold in patients with NC who develop epilepsy may exist,6 which would explain the substantial number of asymptomatic cases of NC in the population.7, 8 An interesting finding of the Del Brutto et al. study is that about 9% of the population had calcifications in the brain and were asymptomatic, which is very similar to a study carried out in rural Mexico,8 which found that 9.1% of apparently healthy participants had calcifications, probably due to NC, and were asymptomatic. Calcifications, of course, could correspond to other pathologies,9 but differential diagnosis would need to be established in a setting, such as a tertiary hospital, where additional diagnostic tools are available. We agree that there is no question that NC is one of many potential causes of epilepsy in some endemic countries, but we would argue that the true unknown question is the following: what proportion of cases of epilepsy are solely caused by NC? Unfortunately, this study and other cross-sectional studies cannot get us closer to answering this question, because temporality cannot be determined. Furthermore, because NC can be both a cause of epilepsy and an incidental finding, other potential causes of epilepsy need to be carefully ruled out before attributing epilepsy to NC. Type of neuroimaging is very important, because use of computed tomography (CT) alone may obscure some causes of epilepsy. It is probably time to recognize that CT alone may be limited in properly assessing etiology in patients with epilepsy for epidemiological studies and its use may lead to biased estimates. Depending on the magnetic resonance imaging (MRI) technique, dual pathology can be demonstrated in between 20% and 30% of cases.10 This raises the question of whether epilepsy could have been wrongly attributed to NC in a substantial number of cases in the present study, as MRI data were available for less than one-third of participants, notwithstanding that calcifications could have corresponded to pathologies other than NC. We argue that it is time to cease initiating more cross-sectional studies that tell us that NC and epilepsy are associated. This question has been answered many times and the answer will not change. It is time to conduct a prospective cohort study enrolling patients with new onset seizures and NC to tell us how many of these patients actually develop epilepsy. Additionally, epidemiological studies must emerge using MRI, at least in the cases selected for neurological examination, to ascertain the etiology of epilepsy. The authors declare no conflicts of interest. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".