Detection of electrographic seizures with continuous EEG monitoring in critically ill patients

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Full frame distilled prediction

Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

Candidate categories

none

Consensus categories

none

Domain

Candidate signal: noneConsensus signal: none

Study design

Candidate signal: Other designConsensus signal: none

Genre

Candidate signal: ReviewConsensus signal: Review

Teacher disagreement score

0.939

Threshold uncertainty score

0.654

Validation status

machine_predicted_unvalidated · codex-gemma-dda1882f352a

Codex and Gemma teacher scores by category

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.

Teacher spread

0.300 · how far apart the two teachers sit on this one work

Validation status

score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

OBJECTIVE: To identify patients most likely to have seizures documented on continuous EEG (cEEG) monitoring and patients who require more prolonged cEEG to record the first seizure. METHODS: Five hundred seventy consecutive patients who underwent cEEG monitoring over a 6.5-year period were reviewed for the detection of subclinical seizures or evaluation of unexplained decrease in level of consciousness. Baseline demographic, clinical, and EEG findings were recorded and a multivariate logistic regression analysis performed to identify factors associated with 1) any EEG seizure activity and 2) first seizure detected after >24 hours of monitoring. RESULTS: Seizures were detected in 19% (n = 110) of patients who underwent cEEG monitoring; the seizures were exclusively nonconvulsive in 92% (n = 101) of these patients. Among patients with seizures, 89% (n = 98) were in intensive care units at the time of monitoring. Electrographic seizures were associated with coma (odds ratio [OR] 7.7, 95% CI 4.2 to 14.2), age <18 years (OR 6.7, 95% CI 2.8 to 16.2), a history of epilepsy (OR 2.7, 95% CI 1.3 to 5.5), and convulsive seizures during the current illness prior to monitoring (OR 2.4, 95% CI 1.4 to 4.3). Seizures were detected within the first 24 hours of cEEG monitoring in 88% of all patients who would eventually have seizures detected by cEEG. In another 5% (n = 6), the first seizure was recorded on monitoring day 2, and in 7% (n = 8), the first seizure was detected after 48 hours of monitoring. Comatose patients were more likely to have their first seizure recorded after >24 hours of monitoring (20% vs 5% of noncomatose patients; OR 4.5, p = 0.018). CONCLUSIONS: CEEG monitoring detected seizure activity in 19% of patients, and the seizures were almost always nonconvulsive. Coma, age <18 years, a history of epilepsy, and convulsive seizures prior to monitoring were risk factors for electrographic seizures. Comatose patients frequently required >24 hours of monitoring to detect the first electrographic seizure.

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The record

Venue

Neurology

Topic

Epilepsy research and treatment

Field

Medicine

Canadian institutions

Columbia College

Funders

not available

Keywords

EpilepsyMedicineElectroencephalographySubclinical infectionComa (optics)Odds ratioAnesthesiaLogistic regressionInternal medicinePediatricsPsychiatry

Has abstract in OpenAlex

yes

API

/api/v1/works/W2082136405