Utility of EEG Activation Procedures in Epilepsy: A Population-Based Study
Bibliographic record
Abstract
PURPOSE: No previous population-based study has addressed the contribution of activation procedures to the yield of epileptiform abnormalities on serial EEGs. We assessed yield of activation-related epileptiform abnormalities and predictors of finding an activation-related abnormality with multiple EEGs in a population-based study of newly diagnosed epilepsy. METHODS: We used the resources of the Rochester Epidemiology Project to identify 449 residents of Rochester, Minnesota with a diagnosis of newly diagnosed epilepsy at age 1 year or older, between 1960 and 1994, who had at least one EEG. Information on all activation procedures (i.e., sleep, hyperventilation, and photic activation) and seizure/epilepsy characteristics was obtained by comprehensive review of medical records. RESULTS: At the first EEG, the yield of epileptiform abnormalities was greatest for individuals 1 to 19 years of age at diagnosis, for each activation procedure. The yield in patients aged 1 to 19 versus ≥20 years was 21.6% versus 10.3% for sleep, 6.5% versus 3.3% for photic stimulation, and 10.3% versus 5% for hyperventilation. Among young people (aged 1-19 years), sleep was associated with an increased likelihood of finding an activation-related abnormality on any EEG. The likelihood of finding an activation-related abnormality on any EEG was decreased for postnatal symptomatic and for unknown etiology. CONCLUSIONS: Among activation procedures, sleep showed the highest yield of epileptiform abnormalities. There was a low yield for photic stimulation and hyperventilation. Within each activation procedure, younger age at diagnosis had the greatest yield. Sleep is the most effective activation procedure, especially in younger patients, and should be performed when possible.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".