MétaCan
Menu
Back to cohort

Validating StatNet EEG as a Reliable and Effective Tool in the Diagnosis of Non-Convulsive Status Epilepticus: Phase 2 (P5.011)

2016· article· en· W2528229209 on OpenAlexaffabout
A Voll, Dianne Dash, Wes Sutherland, Lizbeth Hernández‐Ronquillo, Jose Tellez Zenteno, Farzad Moien Afshari

Bibliographic record

VenueNeurology · 2016
Typearticle
Languageen
FieldComputer Science
TopicMachine Learning in Healthcare
Canadian institutionsSaskatchewan Health AuthorityUniversity of Saskatchewan
Fundersnot available
KeywordsStatus epilepticusElectroencephalographyMedicineEpilepsyNeuroscienceIntensive care medicinePsychologyPsychiatry

Abstract

fetched live from OpenAlex

Objective: 1. Demonstrate the validity of the StatNet portable EEG system in diagnosing seizures and Non-convulsive status epilepticus (NCSE) when conventional EEG is unavailable. 2. Develop clinical criteria to identify patients at risk of NCSE. Background: NCSE is clinically subtle and it dramatically increases morbidity and mortality. This makes it difficult to diagnose without EEG. StatNet EEG provides a quick alternative to conventional EEG, which is often unavailable after hours. Methods: Patients are primarily recruited from the Emergency Department. Each patient received a StatNet EEG and a conventional EEG, when available. We blinded and compared the studies, assessing delay between the studies, setup time, artifact, and detection of abnormalities using conventional EEG as controls. The nonparametric Mann-Whitney two-sample T-test was used. Results are expressed in mean minutes +/- standard deviation. Inter-observer reliability was assessed by Kappa score. Results: 19 patients were collected. Two StatNet EEGs were not interpretable and were excluded. Mean age is 60y ±21.94y (17-93y range). 68[percnt] (N=13) are male. The inter-observer agreement for detection of any abnormalities is 0.54 (0.18, CI=0.19-0.89) for StatNet EEG and 0.73 (0.18, CI=0.37-1.0) for conventional EEG. Inter-observer agreement for epileptiform discharges was 0.76 (0.22, CI=0.33-1.0) for StatNet EEG and 0.76 (0.16, CI=0.43-1.0) for conventional EEG. Inter-observer agreement for NCSE was 1.00 (0, CI=1.00-1.00) for StatNet EEG and 0.64 (0.32, CI=0.0031-1.0) for conventional EEG, in the single identified case. Statnet Electrode placement is significantly shorter: 13:14±5:24 StatNet EEG vs 18:07±5:35 conventional EEG (p=0.02). Conclusions: There is high inter-rater reliability between the conventional and StatNet EEG groups demonstrating that StatNet EEG is a reliable and effective tool, aiding specifically in early recognition and management of NCSE. The potential exists to decrease associated morbidity and mortality. Once StatNet is validated, we will ask for Health Canada approval to implement a stat EEG pathway.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.308
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueNeurologySame topicMachine Learning in HealthcareFrench-language works237,207