Impact of ictal subtraction SPECT and PET in presurgical evaluation
Bibliographic record
Abstract
OBJECTIVE: F-fluorodeoxyglucose positron emission tomography computed tomography (PET) in epilepsy surgery decision making. MATERIALS AND METHODS: A retrospective 3-year study of consecutive patients with resistant focal epilepsy who underwent ISSPECT and PET to evaluate to what extent these modalities influence decisions in epilepsy surgery and outcomes. ISSPECT imaging was performed in 106 patients and 58 (55%) had PET also. The clinical consensus (ClinC) was the final arbiter for decisions. Post-surgical outcomes were collected from follow-up clinics. Non-parametric statistics were used to assess association and logistic regression to evaluate prediction of outcomes. RESULTS: Of 106 patients, 60 were males (57%). MRI was non-lesional in 46 (43%). Concordance with ClinC was seen in 80 patients (76%) for ISSPECT, in 46 patients (79%) for PET, and in 37 patients (64%) for ISSPECT + PET. Fifty-six patients (53%) were planned for intracranial video-electroencephalography monitoring (IVEM). Those with ClinC-PET concordance were likely to proceed to IVEM (p = 0.02). ClinC-PET concordance and ClinC-ISSPECT concordance did not predict decision to proceed to surgery, but VEM-MRI concordance did in lesional cases (p = 0.018). Forty-five (42%) underwent surgery of which 29 had minimum follow-up for 1 year (mean, 20 months; SD, 8) and 22 (76%) had Engel class I outcomes. ClinC-ISSPECT concordance (p = 0.024) and VEM-MRI concordance (p = 0.016) predicted Engel class I outcomes. CONCLUSION: Those with ClinC-PET concordance were more likely to proceed with IVEM. ClinC-ISSPECT concordance and VEM-MRI concordance predicted good surgical outcomes.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".