Exacerbation of Pre-existing Epilepsy by Mild Head Injury: a Five Patient Series
Bibliographic record
Abstract
OBJECTIVE: While the risk of developing seizures following a mild head injury has been reported and is thought to be low, the effect of mild head injury on patients with a pre-existing seizure disorder has not been reported. We present a series of cases where a strong temporal relationship between mild head injury and worsening of seizure frequency was observed. METHODS: Five cases were identified and reviewed in detail. Information was derived from clinic and hospital charts with attention to the degree of injury, pre- and postinjury seizure patterns and frequency. RESULTS: One patient has primary generalized epilepsy and four have localization related epilepsy. Prior to the head injury, three of the patients were seizure free (range: two to 24 years). The patients suffered from mild head injuries with no or transient loss of consciousness and no focal neurological deficits. In all cases, the patients experienced a worsening of seizure control within days of the injury. In one case, the patient's seizure pattern returned to baseline one year after the accident, while in the remaining four cases, the patients continue to have medically refractory seizures. CONCLUSIONS: A close temporal relationship between mild head injury and a worsening of seizure control was observed in five patients with epilepsy. Although further study is required, this observation suggests that a head injury that would be considered benign in the general population can have serious consequences such as recurrence of seizures and medical intractability in patients with epilepsy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".