Hyperkinetic Lingual Movements Resulting from Epileptogenesis: A 13-Patient Cohort Study on Lingual Seizures
Bibliographic record
Abstract
OBJECTIVES: Lingual seizures are rare hyperkinetic tongue movements with significant clinical implications due to their epileptogenic origin. Despite their diagnostic value, these seizures are often underrecognized, particularly when electroencephalographic (EEG) findings are inconclusive. This study aims to characterize their clinical features, EEG patterns, imaging findings and underlying causes, emphasizing the need for increased awareness and improved diagnosis. METHODS: A retrospective review identified patients with isolated lingual seizures or those with additional motor involvement. Data on demographics, seizure characteristics, EEG findings, imaging results and underlying causes were collected and analyzed. Seizures were classified based on the International League Against Epilepsy (ILAE) 2017 framework to refine their clinical and diagnostic profiles. RESULTS: Thirteen patients were identified: 11 with focal-aware and 1 with focal-unaware seizures. Seven had epilepsia partialis continua, and five experienced frequent seizures. Seizure involvement was limited to the tongue in four cases, extended to cranial muscles in seven and affected the tongue, cranial and extremity muscles in two. Significant ictal EEG findings were noted in only three patients with extensive motor involvement. However, nine patients had acute cerebral lesions, associated with glial tumors, encephalitis, chronic gliosis or cortical hemorrhage. CONCLUSIONS: This study provides a detailed characterization of lingual seizures, highlighting their clinical, electrophysiological and imaging features. Given their rarity and underdiagnosis, our findings offer valuable guidance for clinicians, underscoring the importance of improved recognition and diagnostic strategies for this distinct seizure type.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".