Functional imaging: I. Relative predictive value of intracranial electroencephalography
Bibliographic record
Abstract
OBJECTIVE: To gain information on the predictive and prognostic value of magnetic source imaging (MSI), 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography ((18)FDG-PET), and ictal single-photon emission computed tomography (SPECT) as compared with intracranial electroencephalography (ICEEG) localization in epilepsy surgery. METHODS: This work was part of a cohort study of epilepsy surgery candidates not sufficiently localized with noninvasive studies. Of 160 patients enrolled over 4 years, 77 completed ICEEG seizure monitoring. Sensitivity, specificity, and predictive values relative to ICEEG were computed for each modality. RESULTS: Seizures were not captured in five patients. Of the 72 diagnostic ICEEG studies, seizure localization results were 74% localized, 10% multifocal, and 17% nonlocalized. Sixty-one percent were localized to neocortical regions. Depending on patient subgroup pairs, sensitivity ranged from 58 to 64% (MSI), 22 to 40% (PET), and 39 to 48% (SPECT); specificity ranges were 79 to 88% (MSI), 53 to 63% (PET), and 44 to 50% (SPECT). Gains in diagnostic yield were seen only with the combination of MSI and PET or MSI and ictal SPECT. Localization concordance with ICEEG was greatest with MSI, but a significant difference was demonstrated only between MSI and PET. Moderate redundancy was seen between PET and ictal SPECT (kappa = 0.452; p = 0.011). INTERPRETATION: Conclusively positive MSI has a high predictive value for seizures localized with ICEEG. Diagnostic gain may be achieved with addition of either PET or ictal SPECT to MSI. Diagnostic values for imaging tests are lower than "true values" because of the limitations of ICEEG as a gold standard.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 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".