Bibliographic record
Abstract
Purpose of review Epidemiology is the study of the dynamics of a medical condition in a population. There are many shortcomings in the understanding of the epidemiology of epilepsy mostly caused by methodological problems. These include diagnostic accuracy, case ascertainment, and selection bias. In this article recent progress in this area is discussed and suggestions for future research are made. Recent findings It is generally accepted that in developed countries the incidence is around 50/100 000/year. In resource-poor countries, the incidence is likely to be higher. Prevalence of active epilepsy is in the range of 5-10/1000 in most locations, although it might be higher in some isolates. Age-specific incidence rates have changed, with a decrease in younger age groups and an increase in persons above 60 years. The overall prognosis for seizure control is good and over 70% will enter remission. Epilepsy carries an increased risk of premature death particularly in patients with chronic epilepsy. Sudden unexpected death has been increasingly recognized as a major culprit for this increased mortality. Summary There is geographic variation in the incidence of epileptic syndromes likely to be associated with genetic and environmental factors, although as yet causality has not been fully established. The complete range of aetiologies in the general population is not known. Few predictors of outcome are recognized and it is difficult to prognosticate in any individual case. Knowledge is patchy about the epidemiology of sudden unexpected death in epilepsy. Future epidemiological research needs to address these issues if we are to progress.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".