Ictal onset on intracranial <scp>EEG</scp>: Do we know it when we see it? State of the evidence
Bibliographic record
Abstract
OBJECTIVE: A major limitation of intracranial electroencephalography (iEEG) is recording from a confined region. This may falsely localize seizure onset if the distinction between ictal onset zone, proximity, and spread is unclear, or if the ictal rhythm is not clearly identified. Delineation of the ictal onset zone is crucial for surgical success. We appraised the evidence to determine whether specific iEEG ictal patterns are associated with the ictal onset zone. METHODS: We searched Embase for articles in English until September 30, 2014, with MeSH keywords related to intracranially implanted electrodes and seizures. Two authors independently screened abstracts, reviewed full text articles, and abstracted data. The association between seizure outcome and type of ictal onset pattern (IOP), and its extent, location, and spread were explored visually or by univariate analysis when sufficient data were provided. Methodologic quality of each study was assessed. RESULTS: We reviewed 1,987 abstracts from which 21 articles were analyzed. Fifteen IOPs were reported. Low frequency high amplitude repetitive spiking (LFRS) was the most frequently reported IOP by studies that dealt with mesial temporal lobe epilepsy (mTLE) and investigated with depth electrodes. In neocortical epilepsy, low voltage fast activity (LVFA) was the most commonly described IOP. Delta activity was an infrequently reported IOP and was described mostly as a spread pattern. SIGNIFICANCE: LFRS is associated with good surgical outcome in mTLE and has a strong relation with mesial temporal pathology and its severity. LVFA is associated with neocortical temporal epilepsy and focal LVFA is associated with better surgical outcome. Electrodecrement may be associated with regional or widespread onsets. Rhythmic delta is a propagation rhythm rather than an IOP. Focal IOPs and slower propagation times are associated with better outcomes. The quality of the studies is suboptimal and there are methodological problems. Interobserver agreement is poorly documented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".