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Record W2074426217 · doi:10.1017/s0317167100008416

EEG in Suspected Syncope: Do EEGs Ordered by Neurologists Give a Higher Yield?

2009· article· en· W2074426217 on OpenAlexafffundvenue
Laurence Poliquin‐Lasnier, Fraser Moore

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2009
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Syncope and Autonomic Disorders
Canadian institutionsMcGill University
FundersJewish General Hospital
KeywordsElectroencephalographySyncope (phonology)MedicineEpilepsySpecialtyAnesthesiaNeurologyPediatricsCardiologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Prior studies have shown that the electroencephalogram (EEG) is of low diagnostic yield in the evaluation of syncope but have not looked at the yield according to referring physician specialty. The goals of this study were to determine if the yield of the EEG is higher when ordered by neurologists and whether EEGs with abnormal findings resulted in any significant change in patient management. METHODS: We retrospectively reviewed the records of the EEGs requested for a clinical diagnosis of syncope, convulsive syncope, loss of consciousness, or falls from 2003 to 2007 at our institution. We obtained further information from the medical record of patients with an abnormal EEG. RESULTS: Of 517 EEGs meeting our inclusion criteria, only 57 (11.0%) were read as abnormal. No EEG was positive for epileptiform activity and only 9 (1.6%) showed potentially epileptic activity. EEGs ordered by neurologists did not have a higher yield compared to non-neurologists. Five abnormal EEGs resulted in further investigations being ordered. One patient was ultimately started on phenytoin. CONCLUSIONS: EEGs requested for the evaluation of patients with suspected syncope have an extremely low diagnostic yield and do not significantly alter the management of the patients, regardless of the specialty of the referring physician.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.004
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.002
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 head0.021
GPT teacher head0.256
Teacher spread0.235 · 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.

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

Citations22
Published2009
Admission routes3
Has abstractyes

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