Clinical and Radiological Parameters Affecting the Yield of Routine Electroencephalography in Various Indications
Bibliographic record
Abstract
OBJECTIVES: To highlight the significance of various clinical and radiological parameters in association with specific electroencephalographic (EEG) patterns in order to prioritize EEG referrals. METHOD: This retrospective, cross-sectional study was conducted in the neurology department of King Fahad University Hospital, Alkhobar, and involved a review and analysis of EEG and medical records pertaining to 604 patients referred for routine EEG. The data were analyzed using SPSS version 22. An association between various parameters and EEG yield was established. RESULTS: Factors associated with the yield of abnormal EEG patterns were diverse, like generalized tonic-clonic seizures (GTCs) ( P =.05), status epilepticus (SE) ( P =.05), altered level of consciousness (ALC) ( P =.00), abnormal movement ( P =.00), cardiac arrest ( P =.00), prior history of epilepsy ( P =.04), chronic renal disease (CRD) ( P =.03), abnormal neurological exam ( P =.00), and cortical lesions on brain imaging ( P =.00). Among the abnormal EEG patterns, epileptiform activity (EA) in EEG was associated with focal seizures ( P =.03), GTCs ( P =.00), falls ( P =.05), cardiac arrest ( P =.00), a history of epilepsy ( P =.00), and hypoxic ischemic injury ( P =.03). Encephalopathy in EEG was also associated with focal sz ( P =.02), GTCs ( P =.00), SE ( P =.01), ALC ( P =.00), cardiac arrest ( P =.00), history of stroke ( P =.01), and epilepsy ( P =.00). CONCLUSION: Among the studied parameters, patient level of consciousness, neurological exam findings, and neuroimaging findings, with some discrepancies, were found to be the most consistent in predicting the EEG yield. The study demonstrated the value of a proper neurological exam and careful selection of patients to gain the optimum benefit from the routine EEG.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".