B.03 Registered EEG technologists can accurately identify ictal and interictal epileptiform patterns on routine EEG
Bibliographic record
Abstract
Background: Registered EEG technologists (RETs) are trained in both the technical aspects of EEG and in preliminary EEG interpretation. However, there is little research evaluating the accuracy of EEG interpretation by RETs. Methods: Retrospective study of consecutive routine EEG recordings performed at SickKids Hospital. Preliminary reports by RETs and final reports by neurophysiologists were compared in 5 domains: background activity, focal abnormalities, ictal and inter-ictal epileptiform discharges and summary. Results: 500 EEG recordings were analyzed. Sensitivity and specificity of RET reports was high for the assessment of background (85%, 93%), focal slowing (84%, 93%) and inter-ictal epileptiform discharges (92%, 90%). RET reports identified ictal EEG patterns in 32 cases vs. 29 cases identified by neurophysiologists. RET reports were 100% accurate for noting no EEG change for all of 11 cases with non-epileptic events. Conclusions: Preliminary EEG reports by RETs were sensitive and specific for all EEG domains analyzed. In the majority of cases, the preliminary interpretation made by the RET was concordant with the final report of the neurophysiologist. Given these findings, RETs may be able to participate in the screening of routine EEG recordings in order to enhance the productivity of busy EEG laboratories.
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 | 0.003 | 0.021 |
| 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 | 0.001 | 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".