Ictal Forced Repetitive Swearing in Frontal Lobe Epilepsy: Case report and review of the literature
Bibliographic record
Abstract
SUMMARY Introduction.Dominant presentation of ictal forced repetitive swearing has been rarely addressed and could be misdiagnosed. Case report.We report a 45-year-old man with a long history of right frontal lobe epilepsy (FLE) who developed forced repetitive swearing during hypermotor seizures. His seizures were refractory to different antiepileptic drugs (AEDs). Scalp video-EEG telemetry suggested a right frontal epileptic focus. Magnetic resonance imaging (MRI) suggested focal cortical dysplasia (FCD) in the right mesial frontal lobe. Intracranial implantation with video-EEG recordings confirmed seizures originating from the MRI lesion. Patient underwent right frontal lobe resection followed by seizure freedom in the last five years on a single AED. Neuropathology confirmed FCD type IIB. Discussion.The following aspects of the case are discussed: FLE and ictal vocalization, swearing, FLE and aggression. We emphasize the differences among ictal vocalisation, verbal automatism and ictal speech. We propose that ictal swearing might fit a verbal automatism definition. Conclusion.Ictal forced repetitive swearing can be a manifestation of hypermotor seizures in FLE and should not be misdiagnosed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".