Diagnostic Yield of 2-Hour EEG Is Similar With 30-Minute EEG in Patients With a Normal 30-Minute EEG
Bibliographic record
Abstract
PURPOSE: Current literature suggests that longer duration of EEG recording increases the yield of detecting interictal epileptiform discharges. However, optimal duration for a repeat study in patients with initially normal 30-minute EEG is not clear. Thus, the purpose of this study is to determine whether a 2-hour EEG has a diagnostic advantage over a routine 30-minute EEG in detecting epileptiform abnormalities in patients who had a first normal 30-minute EEG. METHODS: This is a single-center, retrospective study done at UT Southwestern Medical Center at Dallas and Parkland Memorial Hospital. The data from 1997 to 2015 were extracted from the existing EEG report database for patients who had a first normal 30-minute EEG recording. EEG was interpreted by board-certified clinical neurophysiologists, who classified each EEG as normal or abnormal, with relevant subsequent subclassification. RESULTS: Over 18 years, a total of 12,425 individual 30-minute EEGs were performed. Of these, 1,023 patients had at least one repeated EEG after the first normal EEG. Among these patients, 763 had a 30-minute EEG as the second study and 260 had a 2-hour EEG as the second study. The yield of epileptiform discharges was 3.3% in the 30-minute EEG group and 4.2% in the 2-hour EEG group (P = 0.5) in the repeating studies. CONCLUSIONS: Two-hour EEG has a similar yield as 30-minute EEG to detect epileptiform discharges in patients with a normal 30-minute EEG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| 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".