Injuries in epilepsy: a review of its prevalence, risk factors, type of injuries and prevention
Bibliographic record
Abstract
Currently, there is intense clinical research into various aspects of the medical risks relating to epilepsy, including total and cause-specific mortality, accidents and injuries in patients with epilepsy and mortality related with seizures. Seizures occurring in precarious situations and resulting in injuries are still an important concern for patients with epilepsy, their employers and their care-givers. Submersion injuries, motor vehicle accidents, burns, and head injuries are among the most feared epilepsy-related injuries. These concerns seem valid because the hallmark of epilepsy, episodic impairment of consciousness and motor control, may occur during interictal EEG epileptiform discharges, even in the absence of a clinical seizure. In addition, psychomotor comorbidity and side effects of antiepileptic drugs may contribute to the risk of injuries in patients with epilepsy. Published risk factors for injuries include the number of antiepileptic drugs, history of generalized seizures, and seizure frequency. In general, epidemiological information about incidence of injuries has been conflicting and sparse. In general, studies focusing on populations with more severe forms of epilepsy tend to report substantially higher risks of injuries than those involving less selected populations. On the other hand, studies based on non-selected populations of people with epilepsy have not shown an increased frequency of injuries in people with epilepsy compared with the general population. Some studies have shown that patients with epilepsy are more frequently admitted to the hospital following an injury. Possible explanations include: more cautious attitude of clinicians toward injuries occurring in the setting of seizures; hospitalization required because of seizures and not to the injuries themselves; and hospitalization driven by other issues, such as comorbidities, which are highly prevalent in patients with epilepsy. Potentially the high rate of hospitalizations could be related with the severity of the injury. This article reviews the best available epidemiological information about injuries, including incidence and risk factors. Also this article reviews information about specific types of injuries such as fractures, burns, concussions, dislocations, etc. Information about accidents in people with epilepsy is also discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".