Is Concussion a Risk Factor for Epilepsy?
Bibliographic record
Abstract
BACKGROUND: Epidemiologic studies have suggested that concussion, or mild traumatic brain injury (mTBI), is associated with a twofold or greater increase in relative risk for the development of post-traumatic epilepsy. To assess the clinical validity of these findings, we analyzed the incidence of epilepsy in a large cohort of post-concussion patients in whom concussion was strictly defined according to international guidelines. METHODS: A retrospective cohort study of 330 consecutive post-concussion patients followed by a single concussion specialist. Exclusion criteria: abnormal brain CT/MRI, Glasgow Coma Scale48 hours. Independent variable: concussion. Outcome measure: epilepsy incidence (dependent variable). RESULTS: The mean number of concussions/patient was 3.3 (±2.5), mean age at first clinic visit 28 years (±14.7), and mean follow-up after first concussion 7.6 years (±10.8). Eight patients were identified whose medical records included mention of seizures or convulsions or epilepsy. Upon review by an epileptologist none met criteria for a definite diagnosis of epilepsy: four had episodic symptoms incompatible with epileptic seizures (e.g., multifocal paraesthesiae, multimodality hallucinations, classic migraine) and normal EEG/MRI investigations; four had syncopal (n=2) or concussive (n=2) convulsions. Compared with annual incidence (0.5/1000 individuals) in the general population, there was no difference in this post-concussion cohort (p=0.49). CONCLUSION: In this large cohort of post-concussion patients we found no increased incidence of epilepsy. For at least the first 5-10 years post-injury, concussion/mTBI should not be considered a significant risk factor for epilepsy. In patients with epilepsy and a past history of concussion, the epilepsy should not be presumed to be post-traumatic.
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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 | 0.006 |
| 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 | 0.001 | 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".