Lateralizing value of interictal epileptiform discharges and other parameters in hypothalamic hamartoma
Bibliographic record
Abstract
Abstract Objective Hypothalamic hamartomas (HHs) are associated with pharmacoresistant epilepsy. Stereotactic radiofrequency thermocoagulation (SRT) shows promise as a disconnecting intervention. Although magnetic resonance imaging (MRI) is typically used to determine the attachment and intervention side, it presents challenges in cases of bilaterally attached HH, where the epileptogenic side is unclear. The lateralizing potential of electroclinical parameters in such cases remains uncertain. This retrospective study evaluates the lateralization value of specific parameters, particularly in patients with unilateral HH, to improve future diagnostics and treatment approaches for bilateral HH. Methods Four lateralizing parameters—semiology, ictal electroencephalography (EEG), and interictal epileptiform discharges during awake (IEDs w ) and sleep states (IEDs s )—were assessed for correlation with HH attachment side using Spearman's ρ. We calculated areas under the curves (AUCs) and cutoffs for left and right IED s prognostic lateralizing value, plotting differences between IED s right and IED s left in a receiver‐operating characteristic(ROC) curve to establish the required preponderance of unilateral IEDs s to differentiate between left and right HHs. Binomial logistic regression was employed to predict the HH attachment side. Results We included 25 patients (2–55 years of age) with mainly unilateral ( n = 22) HHs who underwent SRT and presurgical evaluation. All parameters correlated with HH attachment side (semiology R = −.62, p = .005; ictal EEG R = .51, p = .047; IED s R = .55, p = .018; IED w , R = .61, p = .018). AUC values for right and left IED s were .76 ( p = .047) and .85 ( p = .019), respectively, with cutoffs of .34 and .15. The AUC for “IED s right –IED s left ” was .98 ( p = .0018) with a cutoff of .16. IEDs s and semiology were significant predictors, achieving 88% correct lateralization. Significance IEDs s are promising biomarkers for HH lateralization in unilateral HH. The predominance of unilateral IEDs s suggests ipsilateral HH. Even in cases with predominantly bilateral IEDs s , a slight preponderance of unilateral IEDs s can indicate the attachment side. In addition, combining IEDs s and semiology provides a predictive model for HH lateralization.
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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 | 0.002 |
| 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".