Injuries in people with self‐reported epilepsy: A population‐based study
Bibliographic record
Abstract
PURPOSE: To identify the prevalence of injuries in people with epilepsy (PWE) in the general population. METHOD: We examined the prevalence of injuries obtained through the previously validated, door-to-door Canadian Community Health Survey (CHS) (n = 130,882). The 12-month weighted prevalence of injuries serious enough to limit normal activities was calculated for people with epilepsy and for the general population. Among those reporting injuries, variables of interest were compared in PWE and in the general population using risk ratios (RR) and their 95% confidence intervals (CI(95)). RESULTS: The 12-month weighted prevalence of injuries was not different in PWE (14.9%) and in the general population (13.3%) (RR: 1.1, CI(95): 0.90-1.3). Among individuals reporting injuries, the only significant differences were a lower frequency of sports-related injuries in PWE (RR: 0.7, CI(95): 0.4-0.9), and a three-times higher frequency of hospitalization following injuries in PWE (RR: 3.0, CI(95): 1.3-4.7). Orthopedic injuries were the most frequent type of injury in both groups, but the differences were not significant. Although there were some trends, no significant differences between PWE and the general population were seen with regard to place where injury occurred, mechanism of injury, and number of injuries. CONCLUSIONS: The overall rate of injuries limiting activities did not differ between PWE and the general population. There was a higher rate of injury-related hospital admission in PWE, which could reflect that hospitalization is related to seizures and to comorbidities, and not injuries alone, or a more cautious attitude of clinicians towards injuries in PWE.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".