P.069 The predictive factors of electroencephalograms with epileptiform activity in psychiatric patients
Bibliographic record
Abstract
Background: Psychiatrists commonly use electroencephalogram (EEG) to rule out epilepsy as a cause of psychiatric symptoms. A large number of these studies are normal. Our study aims to identify the predictive factors of an EEG with epileptiform activity in these patients. Methods: We performed a retrospective study of the EEG results and chart reviews of the 208 psychiatric patients at Royal University Hospital in Saskatoon, Saskatchewan from 2013-2015. The EEG results were correlated with several factors known to increase the probability of an abnormal recording including history of seizures, previously abnormal EEGs, imaging abnormalities, medications known to cause epileptiform discharges, electroconvulsive therapy, prematurity, brain infection, childhood febrile seizures, head trauma, and family history. Results: Of the 208 EEGs performed, 176 (84%) were normal (77%) or essentially normal (7%). Epileptiform activity was found in 13 EEGs (6.3%), of which 9 (4.3%) had a previous EEG with epileptiform activity. Focal slowing appeared in 12 EEGs (5.8%), two of which had previous abnormal EEGs. Generalized slowing was found in 7 EEGs (3.4%). Conclusions: We conclude that the majority of EEGs in patients with psychiatric manifestations are normal. The most predictive factor for epileptiform activity in this population is a previous EEG with epileptiform discharges. Other predictive factors are under review.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.011 | 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".