Functional imaging: II. Prediction of epilepsy surgery outcome
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".