Functional imaging: II. Prediction of epilepsy surgery outcome
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Bibliographic record
Abstract
OBJECTIVE: To gain information on the value of magnetic source imaging (MSI), 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET), and ictal single photon emission computed tomography (SPECT) to predict seizure-free outcome following epilepsy surgery in patients who require intracranial electroencephalography (ICEEG). METHODS: This work was part of a prospective observation study of epilepsy surgery candidates not sufficiently localized with scalp EEG and MRI. Of 160 patients enrolled 62 completed ICEEG and subsequent surgical resection. Sixty-one percent resulted in an Engel I seizure-free outcome at a minimum of one-year follow-up (mean = 3.4 years). Sensitivity, specificity, and predictive values were computed for each modality. Multivariate logistical regression was used to identify prediction of surgical outcome by imaging test. RESULTS: MSI sensitivity for a conclusively localized study was 55% with a positive predictive value of 78%. Eliminating non-diagnostic MSI cases (no spikes captured during recording) yielded a corrected negative predictive value of 64%. With available comparison subgroups FDG-PET and ictal SPECT values were similar to MSI. The OR (adjusted for epilepsy and MRI classification) for MSI prediction of seizure-free outcome was 4.4 (p =0.01). In cases with both PET and MSI, the adjusted OR for PET was 7.1 (p <0.01) and for MSI was 6.4 (p = 0.01). In the cases with all three tests (n = 27), ictal SPECT had the highest OR of 9.1 (p = 0.05). INTERPRETATION: MSI, FDG-PET, and ictal SPECT each have clinical value in predicting seizure-free surgical outcome in epilepsy surgery candidates who typically require ICEEG.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it