No Further Yield of Ambulatory EEG for Epileptiform Discharges Beyond 13 Hours
Bibliographic record
Abstract
INTRODUCTION: This study aimed at evaluating the value added by 24-hour ambulatory EEG (AEEG) by comparing the presence of epileptiform discharges (EDs) between the first 30 minutes of recording versus the following 23.5 hours. MATERIALS AND METHODS: A retrospective review of AEEGs of subjects divided into two groups, epilepsy and undiagnosed episodes of loss of consciousness, was conducted. AEEGs were divided into early EEG (E-EEG) (first 30 minutes) and extended EEG (remaining 23.5 hours). Extended EEGs were further divided into segments (S): 31st minute to 8th hour (SI), 9th to 16th hours (SII) and 17th to 24th hours (SIII). Each consecutive segment was reviewed to identify new EDs not seen previously. RESULTS: Fifty-seven AEEGs were included, the median age being 36.3 years, the range being 18.7 to 78.6 years. There were 38 (66.6%) females. The collective yield of AEEG for detecting EDs was 19/57 (33.4%). The yield of E-EEG of new EDs was 5/57 (9%). During extended EEG, the distribution of EDs was as follows: SI, 12/55 (21.8%); SII, 2/43 (4.6%); and SIII 0/41 (0%). The yield, however, did not increase beyond the 13th hour. In undiagnosed episodes of loss of consciousness group (11), yield was 0/11 in all segments. CONCLUSIONS: (1) There was no value added for yield of EDs by extending the EEG recording beyond 13 hours in epilepsy group. (2) The probability of capturing EDs is negligible if the clinical history does not clearly support the diagnosis of seizure or epilepsy.
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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 | 0.007 |
| 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) | 0.002 | 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 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".