Significance of FDG-PET Hypermetabolism in Children with Intractable Focal Epilepsy
Bibliographic record
Abstract
BACKGROUND: Interictal 18F-fluorodeoxyglucose-positron emission topography (FDG-PET) hypometabolism is routinely used in the presurgical workup of children with medically intractable epilepsy (MIE). FDG-PET hypermetabolism, however, is rarely seen, and the significance of this finding in the epilepsy workup is not well established. METHODS: We performed a retrospective study of patients who underwent FDG-PET during the presurgical workup of MIE over a 4-year period, between 1 January 2010 and 31 December 2013, at the Children's Hospital Colorado, CO, USA. RESULTS: Focal FDG-PET hypermetabolism was identified in 7 (2.2%) of 317 patients. The median age was 124 months, all cases with catastrophic epilepsy. Surface electroencephalography (EEG) performed concomitantly with FDG injections revealed ictal EEG discharges in 2 patients, frequent interictal epileptiform discharges (IEDs) in 3, occasional IEDs in 1, and no IEDs in 1. All 7 patients underwent functional hemispherectomies. Histopathology revealed type 1 focal cortical dysplasia in all patients. Six (86%) were completely seizure-free (Engel class I) and 1 had extremely infrequent seizures (Engel class II) (mean follow-up, 47.4 months). CONCLUSION: While a rare finding, interictal PET hypermetabolism does occur, may help identify epileptogenic zones, and assessment to reveal it should be made by concomitant use of surface EEG during PET scans.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".