A Systematic Review of Minimally Invasive Procedures for Mesial Temporal Lobe Epilepsy: Too Minimal, Too Fast?
Bibliographic record
Abstract
BACKGROUND: Cortico-amygdalohippocampectomy (CAH) is effective for mesial temporal lobe epilepsy (mTLE). Concerns regarding surgical morbidity have generated enthusiasm for more minimally invasive interventions. A careful analysis of current data is warranted before widespread adoption of these techniques. OBJECTIVE: To systematically review the use of laser interstitial thermal therapy (LITT), stereotactic radiosurgery (SRS), radiofrequency thermocoagulation (RF-TC), and focused ultrasound for mTLE. METHODS: Major online databases were searched for prospective observational studies, randomized clinical trials, and retrospective studies (>50 patients), including mTLE patients. Outcomes of interest were seizure freedom (Engel I), complications and re-operation rates, and neuropsychological and quality-of-life (QoL) data. RESULTS: Nineteen publications were identified. At ≥6 mo postoperatively, LITT (9/19) Engel I outcomes ranged from 52% to 80%. SRS (3/19) has a latency period (52%-67%, 24-36 mo postoperatively) and the radiosurgery vs. open surgery for epilepsy (ROSE) trial reported inferiority of SRS compared to CAH. RF-TC (7/19) demonstrated variable seizure freedom rates (0%-79%) and high re-operation rates (0%-90%). Twelve studies reported neuropsychological outcomes but QoL (4/19) was not widely reported, and few studies (3/19) assessed both. Study quality ranged from fair to good. CONCLUSION: Based on nonrandomized data, LITT has compelling evidence of efficacy; however, comparisons to surgical resection are lacking. SRS has a latency period and is inferior to CAH (ROSE trial). RF-TC is a less resource-intensive alternative to LITT; however, comparisons of efficacy are limited. Additional studies are needed before minimally invasive procedures can supplant standard surgery.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".