Magnetic resonance‐guided laser interstitial thermal therapy for<scp>drug‐resistant</scp>epilepsy: A systematic review and individual participant data<scp>meta‐analysis</scp>
Bibliographic record
Abstract
Magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) has emerged as a popular minimally invasive alternative to open resective surgery for drug-resistant epilepsy (DRE). We sought to perform a systematic review and individual participant data meta-analysis to identify independent predictors of seizure outcome and complications following MRgLITT for DRE. Eleven databases were searched from January 1, 2010 to February 6, 2021 using the terms "MR-guided ablation therapy" and "epilepsy". Multivariable mixed-effects Cox and logistic regression identified predictors of time to seizure recurrence, seizure freedom, operative complications, and postoperative neurological deficits. From 8705 citations, 46 studies reporting on 450 MRgLITT DRE patients (mean age = 29.5 ± 18.1 years, 49.6% female) were included. Median postoperative seizure freedom and follow-up duration were 15.5 and 19.0 months, respectively. Overall, 240 (57.8%) of 415 patients (excluding palliative corpus callosotomy) were seizure-free at last follow-up. Generalized seizure semiology (hazard ratio [HR] = 1.78, p = .020) and nonlesional magnetic resonance imaging (MRI) findings (HR = 1.50, p = .032) independently predicted shorter time to seizure recurrence. Cerebral cavernous malformation (CCM; odds ratio [OR] = 7.97, p < .001) and mesial temporal sclerosis/atrophy (MTS/A; OR = 2.21, p = .011) were independently associated with greater odds of seizure freedom at last follow-up. Operative complications occurred in 28 (8.5%) of 330 patients and were independently associated with extratemporal ablations (OR = 5.40, p = .012) and nonlesional MRI studies (OR = 3.25, p = .017). Postoperative neurological deficits were observed in 53 (15.1%) of 352 patients and were independently predicted by hypothalamic hamartoma etiology (OR = 5.93, p = .006) and invasive electroencephalographic monitoring (OR = 4.83, p = .003). Overall, MRgLITT is particularly effective in treating patients with well-circumscribed lesional DRE, such as CCM and MTS/A, but less effective in nonlesional cases or lesional cases with a more diffuse epileptogenic network associated with generalized seizures. This study identifies independent predictors of seizure freedom and complications following MRgLITT that may help further guide patient selection.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.020 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".