Nonepileptic, Stereotypical, and Intermittent Symptoms (NESIS) in Patients With Subdural Hematoma: Proposal for a New Clinical Entity With Therapeutic and Prognostic Implications
Bibliographic record
Abstract
BACKGROUND: Transient neurological symptoms (TNS) are frequent in patients with subdural hematomas (SDH) and many will receive a diagnosis of epilepsy despite a negative workup. OBJECTIVE: To explore if patients with TNS and a negative epilepsy workup (cases) evolved differently than those with a positive EEG (controls), which would suggest the existence of alternative etiologies for TNS. METHODS: We performed a single-center, retrospective, case-control study of patients with TNS post-SDH. The demographics and clinical and semiological features of cases and controls were compared. The outcome and response to antiepileptic drugs were also assessed and a scoring system developed to predict negative EEG. RESULTS: Fifty-nine patients with SDH-associated TNS were included (39 cases and 20 controls). Demographic characteristics were comparable in both groups. Dysphasia and prolonged episodes were associated with a negative EEG. Clonic movements, impaired awareness, positive symptomatology, complete response to antiepileptic drugs, and mortality were associated with a positive EEG. Using semiological variables, we created a scoring system with a 96.6% sensitivity and 100% specificity in predicting case group patients. The differences observed between both groups support the existence of an alternative etiology to seizures in our case group. We propose the term NESIS (NonEpileptic, Stereotypical, and Intermittent Symptoms) to refer to this subgroup and hypothesize that TNS in these patients might result from cortical spreading depolarization. CONCLUSION: We describe NESIS as a syndrome experienced by SDH patients with specific prognostic and therapeutic implications. Independent validation of this new entity is now required.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".