Surgical Treatment of Hypothalamic Hamartoma Causing Refractory Epilepsy: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Hypothalamic hamartomas (HHs) are a known cause of refractory focal epilepsy. Advancement in microsurgical techniques and introduction of stereotactic ablative methods have led to improved complication rates, but the effect on seizure control is still to be determined. In this systematic review, we present a thorough analysis of published literature on the outcomes of various surgical treatments of HHs for refractory epilepsy. METHODS: A literature search using the MedLine, SCOPUS and Cochrane databases was conducted. All English language studies describing surgical treatment of HH with refractory epilepsy, with a minimum of three patients and a follow-up of at least one year, were identified. RESULTS: An initial selection of 55 studies was reduced to 41 after combining studies from the same groups; 14 open, 4 endoscopic, 8 Gamma Knife radiosurgery (GKRS), 9 laser interstitial thermal therapy (LITT) and 6 radiofrequency thermocoagulation (RF-TC) studies were included. From a total of 832 patients, 209 underwent open (25.1%), 80 endoscopic (9.6%), 124 GKRS (14.9%), 229 LITT (27.5%) and 190 RF-TC (22.8%). Engel I or ILAE 1 or 2 was achieved in: open 115 (55.0%), endoscopic 38 (47.5%), GKRS 49 (39.5%), LITT 176 (76.9%) and RF-TC 128 (67.4%). Invasive surgeries (open and endoscopic) had a higher incidence of neurological complications (27.0%) than ablative surgeries (GKRS, LITT, RF-TC) (7.2%). Reoperation rates were higher for ablative surgeries (23.8%) than invasive surgeries (9.0%). CONCLUSION: Surgical treatment of HH causing refractory epilepsy is effective. RF-TC and LITT surgery types have the highest Engel class I outcomes, and ablative surgeries have a lower neurological complication profile compared to open and endoscopic approaches.
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 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".