Is concussion a risk factor for epilepsy?
Bibliographic record
Abstract
Objective It has been stated that concussion is a risk factor for the development of post-traumatic epilepsy, but few data exist to support or refute this statement. We analysed the incidence and prevalence of epilepsy in a large cohort of post-concussion patients. Design Retrospective cohort study. Setting Academic tertiary care centre. Participants 330 consecutive post-concussion patients followed by one concussion specialist (CHT). Exclusion criteria: abnormal brain CT/MRI, GCS ≤ 12 >1 hr post-injury, hospitalisation>48 hrs. Mean number of concussions/patient 3.3 (±2.5), mean age at first clinic visit 28 years (±14.7), mean follow-up after first concussion 7.6 years (±10.8). Assessment of risk factor Independent variable, concussion. Outcome measures Epilepsy incidence (dependent variable); prevalence. Main results Eight patients had medical record documentation of seizures or convulsions or epilepsy. Upon detailed review by an epilepsy specialist (RW) none met criteria for a definite diagnosis of epilepsy: 4 had episodic symptoms incompatible with epileptic seizures (multifocal paraesthesiae, multimodality hallucinations, classic migraine) and normal EEG/MRI investigations; 4 had clear histories of syncopal (n=2) or concussive (n=2) convulsions. Compared to epilepsy prevalence (6.6/1000 individuals) and annual incidence (0.5/1000 individuals) in the general population, there was no difference in this post-concussion cohort (Chi-square=0.218, d.f.1, corr.cont., p=0.64). Conclusions In this large cohort of post-concussion patients we found no evidence of an increased incidence of epilepsy, and the prevalence of epilepsy was lower than in the general population. Concussion is not a significant risk factor for the development of post-traumatic epilepsy, at least in the first decade post-injury. Competing interests None.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".