Triphasic Waves Versus Nonconvulsive Status Epilepticus: EEG Distinction
Bibliographic record
Abstract
BACKGROUND: Triphasic waves (TWs) and generalized nonconvulsive status epilepticus (GNCSE) share morphological features that may create diagnostic ambiguity. OBJECTIVE: To describe electroencephalographic differences between TWs and GNCSE. METHODS: We retrospectively compared the electroencephalograms (EEGs) of two groups of patients presenting with decreased level of consciousness; those with TWs associated with metabolic encephalopathy and those with GNCSE. We studied the following: demographics, etiology and EEG morphological features. All EEGs were classified blindly (TWs or GNCSE) by two expert EEGers. Agreement between experts and concordance with clinical diagnosis were measured. RESULTS: We analysed 87 EEGs (71 patients) with TWs and 27 EEGs (13 patients) with GNCSE. Agreement between experts and concordance with clinical diagnosis were excellent. When compared to TWs, epileptiform discharges associated with GNCSE had a higher frequency (mean=2.4Hz vs 1.8Hz) (p<0.001), a shorter duration of phase one (p=0.001), extra-spikes components (69% vs 0%) (p<0.001) and less generalized background slowing (15.1% vs 91.1%) (p<0.001). Amplitude predominance of phase two was common with TWs (40.8% vs 0%) (p=0.01). Lag of phase two was absent in all cases of GNCSE but present in 40.8% of patients with TWs. Noxious or auditory stimulation frequently increased the TWs (51%) while it had no effect on the epileptiform pattern (p=0.008). CONCLUSIONS: Certain EEG morphological criteria and the response to stimulation are very helpful in distinguishing TWs from GNCSE.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".