P.076 Quantitative EEG in Canada: a national technologist survey
Bibliographic record
Abstract
Background: Burgeoning EEG demand has largely gone unmet with insufficient supply of manpower and equipment. Quantitative EEG (QEEG) may help compress large volumes of data for expedited review. We sought to determine the current use of QEEG in Canada through a national EEG technologist survey. Methods: A 10-item questionnaire was administered to participants at the 2016 meeting of the Canadian Association of Electroneurophysiology Technologists, which occurred in parallel with the Canadian Neurological Sciences Federation meeting. Results: A response rate of 63% (14/22) represented 12 institutions (11 adult, 6 paediatric) over six provinces with 73% of the national population. Only academic institutions (9/12) used QEEG, representing five provinces with 70% of the national population. Most institutions generated QEEG either real-time or retrospectively in the critical care and epilepsy monitoring units for long-term monitoring and automated seizure detection. The most used trends were spectrographic, seizure detection, and artifact detection. Montage use, QEEG duration, and timebase settings were highly variable. Conclusions: QEEG is in surprisingly frequent use across Canada. There is no consensus on optimal QEEG use, which mirrors uncertainty in the literature. The relative ubiquity of QEEG in Canada offers promise for collaborative multicentre research into unlocking the full potential of QEEG in enhancing patient care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".