How Often Does Routine Pediatric EEG Have an Important Unexpected Result?
Bibliographic record
Abstract
BACKGROUND: Electroencephalogram recordings are requested for the assessment of many childhood disorders. To assess the utility of the EEG in children, we studied how often routine EEG results can be correctly predicted from the EEG requisition. METHOD: Five hundred consecutive initial EEG requests from the IWK Grace Health Centre from two time epochs were examined. All EEGs were 16 channel (10-20 electrode system). Based only on the requisition (patient demographics, referring physician, and reason for EEG), we coded our prediction of the result and then the actual result. When results were discordant from prediction, a judgment was made about the potential importance of the result. RESULTS: Overall, EEG results were correctly predicted in 81%. Prediction for all nonepilepsy reasons was accurate in 91% (n=320) and 96% for paroxysmal nonepileptic events (n=158) but only 59% for epileptic disorders (n=141) (p<0.0001). Neurologists ordered 45% of EEGs, pediatricians 32%, and GP's 17%. Predictions were least accurate for neurologists' requests (p<0.006) however, neurologists were more likely to request EEG for epileptic disorders (p<0.0001). Age of the child and urban versus rural address did not affect the accuracy of prediction. CONCLUSION: Results of routine pediatric EEG for most nonepilepsy reasons appear highly predictable and therefore, possibly of little value to an experienced clinician. When requested for epilepsy, this "ancient" test remains full of surprises.
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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 | 0.025 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".