The incidence of injuries in persons with and without epilepsy—A population‐based study
Bibliographic record
Abstract
PURPOSE: To compare the 1-year population-based incidence and types of injuries in persons with and without epilepsy. METHODS: Three administrative databases (inpatient visits, physician claims, and emergency room visits) were linked from fiscal years 1996-2003 using a provincial insurance plan registry, which captures 99% of a population of 1.4 million in a large Canadian health region. Epilepsy cases (all age groups) from fiscal year 1996-2002 were identified. Three people without epilepsy were matched to one person with epilepsy for age (±1 year) and sex. Injuries were defined as any of 16 types of injuries for which medical attention was sought that occurred within fiscal year 2003. RESULTS: Eight thousand eight hundred ninety subjects with epilepsy were identified and matched to 26,670 controls for age and sex. The mean age was 37.4 years (range 0.01-96.4 years), and 51.3% of subjects were male. The 1-year incidence of one or more injuries was 20.6% among persons with epilepsy and 16.1% among those without epilepsy (p < 0.001). Of the 16 types of injuries studied, 11 were higher in persons with epilepsy compared to those without epilepsy, and included fractures, crushing injuries, intracranial injuries, other types of head injuries, and multiple injuries. The difference was still significant after adjusting for age, gender, and comorbidities. DISCUSSION: The 1-year incidence of injuries in this study was greater in persons with epilepsy compared to those without epilepsy, for nearly all injury types. Injury prevention should be discussed during routine visits in persons with epilepsy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".