Epilepsy Care in Latin America and the Caribbean: Overcoming Challenges and Embracing Opportunities
Bibliographic record
Abstract
The burden of epilepsy in the Latin America and the Caribbean (LAC) region causes a profound regional impact on the health care system and significantly contributes to the global epilepsy burden. As in many other resource-limited settings worldwide, health care professionals and patients with epilepsy in LAC countries face profound challenges due to a combination of factors, including high disease prevalence, stigmatization of epilepsy, disparities in access to care, limited resources, substantial treatment gaps, insufficient training opportunities for health care providers, and a diverse patient population with varying needs. This article presents an overview of the epidemiology of epilepsy and discusses the principal obstacles to epilepsy care and key contributors to the epilepsy diagnosis and treatment gap in the LAC region. We conclude by highlighting various initiatives across different LAC countries to improve epilepsy care in marginalized communities, listing strategies to mitigate treatment gaps and facilitate better health care access for patients with epilepsy by enhancing the epilepsy workforce.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".