Audit of EEG Reporting Temporal Abnormalities
Bibliographic record
Abstract
BACKGROUND: To ensure the overall quality of our electroencephalogram (EEG) laboratory, we decided to perform an audit of EEGs interpreted at our institution, focusing initially on EEGs reporting temporal abnormalities. METHODS: Reports of all EEGs performed between January 1st and June 30th, 2006 were reviewed in order to identify tracings mentioning abnormalities in the temporal regions. These records were then independently reviewed by two epileptologists on two distinct occasions, separated by an interval of at least six months. If the recording was considered normal after this process, the cause for misinterpretation was identified and the patient's chart was reviewed to determine if he was epileptic or not based on available evidence until June 2009. RESULTS: In the first half of 2006, 143 out of 773 EEGs mentioned temporal abnormalities (18.5%). In general, intra- and interrater agreement ratios between our two epileptologists were moderate to substantial for normality, presence of epileptic activity and presence of slowing. Forty-five recordings (31.5%) were reported as normal independently by them on two distinct sittings six months apart. The most common causes for misinterpretation were the presence of benign epileptiform variants, normal sharply contoured patterns of somnolence or hyperventilation. Chart review confirmed that most were non-epileptic patients (60% non-epileptic, 27% epileptic, 13% unknown). CONCLUSION: Moderate to substantial intra- and interrater agreement as well as frequent misinterpretation of physiological variants indicate that some corrective measures need to be implemented to improve the consistency of EEG interpretation amongst our group of electroencephalographers.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".