Lateralization of Temporal Lobe Epilepsy (TLE) and Discrimination of TLE from Extra‐TLE Using Pattern Analysis of Magnetic Resonance Spectroscopic and Volumetric Data
Bibliographic record
Abstract
PURPOSE: To examine whether or not pattern analysis of magnetic resonance volumetric (MRVol) and proton magnetic resonance spectroscopic imaging (1H-MRSI) data would enable (a) the accurate lateralization of temporal lobe epilepsy (TLE) and (b) the discrimination of TLE from extratemporal epilepsy (E-TLE). METHODS: For lateralization analysis, we used data from 150 nonforeign tissue lesional TLE patients [88 left-sided (L-TLE), 46 right-sided (R-TLE), and 16 bilateral (Bi-TLE)]. For the discrimination of TLE from E-TLE, we used data from 174 patients (145 with unilateral TLE, 14 with unilateral E-TLE, and 15 with widespread epileptogenic zones involving both the TL and extra-TL regions-multilobar epilepsy). A series of "leave-one-out" cross-validated linear discriminant analyses were performed using the MRVol and 1H-MRSI data sets to lateralize TLE and discriminate it from E-TLE. RESULTS: Lateralization: The leave-one-out linear discriminant analyses were able to correctly lateralize (with a posterior probability >0.50) 120 (90%) of the 134 L-TLE and R-TLE patients. Imposing higher posterior probability (>0.95) increased accuracy of lateralization to 98%, with only two discordant cases who underwent surgery on the side of electroencephalogram, and both had bad outcome. Discrimination: the leave-one-out linear discriminant analyses were able to correctly classify (with a posterior probability >0.50) 142 (89%) of the 159 TLE and E-TLE patients. Accuracy increased slightly as higher posterior probability cutoffs were imposed, with fewer patients being classified. CONCLUSIONS: Pattern analysis of 1H-MRSI and MRVol data can accurately lateralize TLE. Discriminating TLE from E-TLE was less accurate, probably due to the presence of temporal lobe damage in some patients with E-TLE reflecting dual pathology.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".