Continuous EEG Monitoring in Canadian Hospitals
Bibliographic record
Abstract
Background and Objectives: Continuous EEG (cEEG) is the gold standard for diagnosing nonconvulsive seizures (NCSs) and nonconvulsive status epilepticus (NCSE) in critically ill patients, with NCSE occurring in 8%-10% of patients with unexplained coma. Untreated NCSs are associated with secondary brain injury, as well as increased mortality and morbidity. cEEG monitoring allows clinicians to identify more than twice the number of seizures compared with a 30-min routine EEG recording. However, there are limited data on cEEG practices in Canadian hospitals. The aim of this study was to evaluate the availability, indications, and barriers to cEEG access in Canada. Methods: A national cross-sectional survey was distributed to EEG laboratory directors and physicians who interpret cEEGs to assess cEEG monitoring practices in Canadian adult hospitals. The survey evaluated institutional cEEG availability, clinical applications, and technical infrastructure. Results: Among 1,267 adult hospitals in Canada, only 92 hospital networks (9%) were identified as having an EEG laboratory. Twenty-four were identified as potentially offering cEEG monitoring, and a survey was sent to a physician at these institutions. Responses were received from 22 institutions (92% response rate), with 19 hospital networks reporting cEEG availability-representing just 2% of Canadian hospitals. Geographic disparities were significant, with 3 provinces and all 3 territories lacking cEEG access. Among tertiary care hospitals, only 68% reported cEEG availability. Barriers included insufficient EEG technologist coverage and prolonged processing periods for 24-hour EEG recordings. Most institutions lacked standardized guidelines, were unable to perform new cEEG hookups after regular work hours, and did not have access to abbreviated montages when cEEG was unavailable. Discussion: cEEG availability in Canada is highly limited, including at tertiary care centers, with significant geographic inequities and operational barriers. Most Canadian hospitals do not meet guideline standards for cEEG use. These findings highlight the need for systemic changes to improve cEEG access and align Canadian cEEG practices with international standards.
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 | 0.005 |
| 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) | 0.005 | 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".