P.046 Increasing EEG monitoring in the pediatric ICU - benefits and barriers
Bibliographic record
Abstract
Background: Non-convulsive seizures are common in critically ill patients and are best detected by continuous EEG (cEEG) monitoring. A recent consensus statement from the American Clinical Neurophysiology Society (ACNS) outlines the indications for EEG monitoring in critically ill patients. Our aim was to assess adherence to these indications, barriers to cEEG utilization as well as to optimize cEEG monitoring in critically ill children. Methods: We conducted a retrospective review of electronic medical records, analyzing patients admitted to the PICU from January 1st until June 23rd 2018, followed by an 8-week mentorship period, consisting of educational interventions as well as daily patient rounds to help identify patients meeting cEEG monitoring criteria. Results: Prevalence of patients meeting cEEG monitoring indications were similar in both the retrospective and mentorship period (18% vs. 23%). During the retrospective period, 23% of patients received cEEG monitoring, reaching 100% at the end of the mentorship period. The median delay for initiation of monitoring was 17 hours, largely due to restrictions in the availability of technologists. All cEEGs performed informed anti-convulsive management. Conclusions: An educational intervention was effective in increasing PICU cEEG monitoring. However, limited hours of technologist availability represented the largest barrier to timely cEEG monitoring.
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 | 0.005 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".