Outcome of Surgery for Hypothalamic Hamartoma-Related Epilepsy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There is a paucity of data directly comparing the outcome of surgical techniques available for the treatment of hypothalamic hamartomas (HHs). This study aims to evaluate the safety and efficacy of commonly used surgical approaches in the treatment of HH-related epilepsy. METHODS: A systematic review and individual participant data (IPD) meta-analysis was conducted. The PubMed, Embase, and Scopus online databases were searched without any date restrictions for original studies with more than 1 participant reporting on patients with HH-related epilepsy who underwent surgical treatment. Random-effects modeling was used to calculate the pooled proportions of seizure freedom (Engel I) at the last follow-up. IPD were used to perform mixed-effects logistic regression to identify predictors of seizure freedom and major postoperative complications. RESULTS: = 0.045) being associated with a lower likelihood of major complications. DISCUSSION: MRgLITT and RFTC offer superior efficacy and safety compared with open microsurgery and should be considered as first-line options. Despite its lower efficacy, SRS is associated with few reported long-term complications, making it a viable alternative for select cases, such as small HHs with good baseline functioning. Direct comparisons between techniques are limited by short follow-up durations in RFTC and MRgLITT cohorts. Further large-scale, multicenter studies directly comparing these modalities are warranted.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".