EEG in Suspected Syncope: Do EEGs Ordered by Neurologists Give a Higher Yield?
Bibliographic record
Abstract
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 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 | 0.003 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".