Delivering epilepsy care in low-resource settings: the role of technology
Bibliographic record
Abstract
INTRODUCTION: The implementation of technology in the field of epileptology has traditionally focused on its use for diagnosis and treatment and has, unsurprisingly, been capital-intensive, making it therefore mainly implementable in advanced high-income countries. Because of technological innovations over the past 20 years there has been almost a paradigm shift, particularly in access to and the potential for implementing relevant technology in lesser developed environments. Nearly 80% of people living with epilepsy live in low and middle-income countries. AREAS COVERED: The challenge and the purpose of this paper is to discuss how technology can be implemented into lesser-resourced contexts not only cost-effectively but in a cost-saving way while also building capacity and thus sustainability. EXPERT OPINION: The rate of technological advancement presents the risk of progressive widening of the technology and care gaps between advanced and lesser developed regions. Implementing technology is both about finding relevant appropriate technologies for the individual contexts of a diverse range of countries but also about repurposing low-tech technologies for application in epilepsy care in these areas. Finally exciting advances such as autonomous driving, digital twinning and robotic surgery will likely transform epilepsy care in several lower-resourced settings in the next 5-10 years.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".