A single‐center learning curve for stereotactic laser amygdalohippocampotomy and a surgical framework to manage failures
Bibliographic record
Abstract
OBJECTIVE: Stereotactic laser amygdalohippocampotomy (SLAH) is a minimally invasive procedure for mesial temporal lobe epilepsy that preserves more tissue than open procedures. As a result, although patients have better functional outcomes, more patients do not achieve seizure freedom. The rate at which this occurs is evolving with improved surgical practices. However, the risks and benefits of further surgical management for these patients remains a question with limited data to guide decision-making. METHODS: We retrospectively reviewed a continuous series (2011-2019) of SLAH operations at our institution to determine trends in surgical management, identifying cases where further surgery was performed. Pre-operative and follow-up seizure, cognitive, and functional data, and surgical complications were collated. RESULTS: Of 108 patients undergoing primary SLAH, 21 (19%) underwent further surgery (23 procedures). Stereo-electroencephalography (SEEG) informed seven procedures (30%). There was a trend for quicker SLAH failure in the earlier patients. Similarly, surgical chronology was associated with progression to repeat surgery (p = .007). At 1-year follow-up, 6 of 13 patients (46%) achieved seizure freedom after repeat SLAH and 5 of 8 patients (63%) achieved seizure freedom after anterior temporal lobectomy (ATL), one of whom had failed two SLAHs. Two of three patients undergoing an ablation outside the mesial temporal lobe achieved seizure freedom at 1 year. Neuropsychological sequelae were more prevalent with ATL than SLAH, including decline in visual naming (p = .01) and functional status (p = .007). SIGNIFICANCE: Repeat SLAH and ATL post-SLAH are both practicable and can be effective. Surgical experience, risk to cognition, and marginal benefit relative to existing improvement are principal considerations for further 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".