Seizure identification by clinical description in temporal lobe epilepsy
Bibliographic record
Abstract
OBJECTIVE: To determine the accuracy of the clinical history performed by epileptologists in the identification of seizures in patients with suspected temporal lobe epilepsy. METHODS: The clinical and EEG telemetry (EEGT) monitoring data of 88 patients with suspected refractory temporal lobe seizures referred for evaluation of epilepsy surgery were prospectively evaluated. All clinical events obtained by history in these patients were adjudicated as being a seizure or not by two blinded (without access to EEG data) independent epileptologists. Each clinical event was then matched with the corresponding clinical event recorded with EEG telemetry in the epilepsy monitoring unit (gold standard). Sensitivity, specificity, overall accuracy, predictive value, and interrater agreement for the clinical assessment were obtained. RESULTS: Of 357 clinically different events, 175 (49%) were reproduced in the epilepsy monitoring unit. Only 10 events were misidentified by history as being a seizure or not, resulting in an overall clinical accuracy of 94%. Epileptologists' sensitivity for seizure identification was 96% (95% CI 92, 98%) but specificity was only 50% (95% CI 22, 79%). Accuracy for complex partial seizures and generalized seizures was higher than for simple partial seizures (SPS). Misidentification occurred only with SPS and nonepileptic events. Agreement beyond chance among epileptologists was good. CONCLUSION: In this selected group of patients with temporal lobe epilepsy, seizure identification by clinical history is highly accurate. Epileptologists rarely miss seizures (high sensitivity) but more often overcall nonepileptic events as seizures (low specificity).
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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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".