The meaning of interictal spikes in temporal lobe epilepsy
Bibliographic record
Abstract
In neurologic practice, interictal spikes on EEG recording are used to confirm a diagnosis of epilepsy, and to help identify a patient’s epilepsy syndrome. In the early days, spikes also provided the main functional localizing evidence in the presurgical evaluation of patients with medically intractable epilepsy.1 Nowadays, focal unilateral spikes still are a helpful clue, along with many others, for localizing the region considered for surgical resection. However, clinicians too often consider spikes as something that happens on the EEG rather than something that happens in the patient’s brain. Spikes are a biomarker for the underlying pathophysiology of the epileptic condition. In this issue, Krendl et al.2 illuminate the significance of interictal spikes by using simple but effective measures in patients with unilateral hippocampal atrophy. Their novel finding was that frequent spikes (>60/h) were a strong predictor of an unfavorable surgical outcome, whereas less frequent spikes predicted a better outcome. Although unilateral predominance of interictal spikes is often considered helpful in lateralizing the seizure onset zone, in …
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".