Bibliographic record
Abstract
Only one prospective, controlled study has compared the risk of accidental injury in persons with epilepsy to controls without seizures. A mildly increased risk in the epilepsy group was found, predominantly due to injuries that result directly from a seizure. With regard to injury type, this study found significantly higher rates of only head and soft tissue injury; however, most injuries were minor. Several retrospective, population-based studies have suggested increased rates of more serious injury types. Submersion injury has a high mortality; the risk of submersion in children with epilepsy is 7.5-13.9 fold higher than in the general population. The risk of fracture is elevated approximately twofold, either resulting directly from seizure-induced injury or predisposed by drug-induced reduction in bone mineral density. Burns due to seizures account for between 1.6% and 3.7% of burn unit admissions. The risk of motor vehicle accidents in drivers with epilepsy also appears increased, albeit marginally. Several factors predispose to a higher risk of injury among those with epilepsy. Seizures resulting in falls increase the risk of concussion and other injuries. Higher seizure frequency, lack of a prolonged seizure-free interval, comorbid attention deficit disorder, or cognitive handicap may also increase the risk of injury. While some restrictions are necessary to protect the safety of the person with epilepsy, undue limitations may further limit achievement of independence. Given the high morbidity and mortality of submersion injury, those with active epilepsy should bathe or swim only with supervision; however, showering is a reasonable option. Appropriate vitamin D and calcium supplementation and periodic measurement of bone mineral density in those at risk for osteopenia are recommended.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".