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Record W2756399640 · doi:10.1080/21646821.2017.1353799

No Further Yield of Ambulatory EEG for Epileptiform Discharges Beyond 13 Hours

2017· article· en· W2756399640 on OpenAlexafffund
Maria Siddiqi, S. Nizam Ahmed

Bibliographic record

VenueThe Neurodiagnostic Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsAmbulatoryElectroencephalographyYield (engineering)EpilepsyMedicinePsychologyAudiologyAnesthesiaNeuroscienceInternal medicineMaterials science

Abstract

fetched live from OpenAlex

INTRODUCTION: This study aimed at evaluating the value added by 24-hour ambulatory EEG (AEEG) by comparing the presence of epileptiform discharges (EDs) between the first 30 minutes of recording versus the following 23.5 hours. MATERIALS AND METHODS: A retrospective review of AEEGs of subjects divided into two groups, epilepsy and undiagnosed episodes of loss of consciousness, was conducted. AEEGs were divided into early EEG (E-EEG) (first 30 minutes) and extended EEG (remaining 23.5 hours). Extended EEGs were further divided into segments (S): 31st minute to 8th hour (SI), 9th to 16th hours (SII) and 17th to 24th hours (SIII). Each consecutive segment was reviewed to identify new EDs not seen previously. RESULTS: Fifty-seven AEEGs were included, the median age being 36.3 years, the range being 18.7 to 78.6 years. There were 38 (66.6%) females. The collective yield of AEEG for detecting EDs was 19/57 (33.4%). The yield of E-EEG of new EDs was 5/57 (9%). During extended EEG, the distribution of EDs was as follows: SI, 12/55 (21.8%); SII, 2/43 (4.6%); and SIII 0/41 (0%). The yield, however, did not increase beyond the 13th hour. In undiagnosed episodes of loss of consciousness group (11), yield was 0/11 in all segments. CONCLUSIONS: (1) There was no value added for yield of EDs by extending the EEG recording beyond 13 hours in epilepsy group. (2) The probability of capturing EDs is negligible if the clinical history does not clearly support the diagnosis of seizure or epilepsy.

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)0.004
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 score0.088
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.034
GPT teacher head0.315
Teacher spread0.281 · 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

Citations4
Published2017
Admission routes2
Has abstractyes

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