<scp>FDG</scp>‐<scp>PET</scp> and magnetoencephalography in presurgical workup of children with localization‐related nonlesional epilepsy
Bibliographic record
Abstract
PURPOSE: 2-[18F]Fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) and magnetoencephalography (MEG) may assist in identifying the epileptogenic zone in children with nonlesional localization-related epilepsy. The aim of this study was to evaluate sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of FDG-PET, MEG, FDG-PET + MEG, and FDG-PET/MEG in children with nonlesional localization-related epilepsy. METHODS: Twenty-six children with localization-related epilepsy and who had normal or subtle changes on magnetic resonance imaging (MRI) underwent FDG-PET and MEG. Twenty-two patients had surgical resection, and surgical outcome was assessed using Engel classification. In patients with Engel I seizure outcome, we assessed the sensitivity, specificity, PPV, and NPV of lobar localization of MEG, FDG-PET, FDG-PET + MEG, and FDG-PET/MEG. KEY FINDINGS: Sixteen (72.7%) of 22 had Engel I seizure outcome. MEG was concordant with surgical resection in 18 patients, 14 had Engel I, and four had Engel II-IV outcomes. MEG was nonlocalizing or nonconcordant in four patients; two patients had Engel I and two had Engel II-IV outcomes. FDG-PET was concordant with surgical resection in 14 patients; 9 had Engel I outcome, and 5 had Engel II-IV outcome. FDG-PET was nonlocalizing or nonconcordant in seven patients with Engel I, and one with Engel III outcome. The sensitivity, specificity, PPV, and NPV of MEG were 85.0%, 99.1%, 94.4%, and 97.3%, respectively. The sensitivity, specificity, PPV, and NPV of FDG-PET were 65.0%, 94.4%, 68.4%, and 93.6%, respectively. There was no significant difference between MEG and FDG-PET for concordance with surgical resection (χ(2) = 2.794, p = 0.095). FDG-PET + MEG, defined as two tests concordant with surgical resection, had reduced sensitivity and NPV, but increased specificity and PPV (55.0%, 92.3%, 100%, and 100%, respectively) relative to individual tests. FDG-PET/MEG, defined as one or both test(s) concordant with surgical resection, had increased sensitivity and NPV but reduced specificity (95.0%, 99.0%, and 93.5%, respectively) relative to individual tests. SIGNIFICANCE: The two tests FDG-PET and MEG were complementary in the assessment of children with localization-related epilepsy, particularly when one test was nonlocalizing or nonconcordant.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".