Frameless robot-assisted stereoelectroencephalography in children: technical aspects and comparison with Talairach frame technique
Bibliographic record
Abstract
OBJECTIVE Robot-assisted stereoelectroencephalography (SEEG) is gaining popularity as a technique for localization of the epileptogenic zone (EZ) in children with pharmacoresistant epilepsy. Here, the authors describe their frameless robot-assisted SEEG technique and report preliminary outcomes and relative complications in children as compared to results with the Talairach frame-based SEEG technique. METHODS The authors retrospectively analyzed the results of 19 robot-assisted SEEG electrode implantations in 17 consecutive children (age < 17 years) with pharmacoresistant epilepsy, and compared these results to 19 preceding SEEG electrode implantations in 18 children who underwent the traditional Talairach frame-based SEEG electrode implantation. The primary end points were seizure-freedom rates, operating time, and complication rates. RESULTS Seventeen children (age < 17 years) underwent a total of 19 robot-assisted SEEG electrode implantations. In total, 265 electrodes were implanted. Twelve children went on to have EZ resection: 4 demonstrated Engel class I outcomes, whereas 2 had Engel class II outcomes, and 6 had Engel class III-IV outcomes. Of the 5 patients who did not have resection, 2 underwent thermocoagulation. One child reported transient paresthesia associated with 2 small subdural hematomas, and 3 other children had minor asymptomatic intracranial hemorrhages. There were no differences in complication rates, rates of resective epilepsy surgery, or seizure freedom rates between this cohort and the preceding 18 children who underwent Talairach frame-based SEEG. The frameless robot-assisted technique was associated with shorter operating time (p < 0.05). CONCLUSIONS Frameless robot-assisted SEEG is a safe and effective means of identifying the EZ in children with pharmacoresistant partial epilepsy. Robot-assisted SEEG is faster than the Talairach frame-based method, and has equivalent safety and efficacy. The former, furthermore, facilitates more electrode trajectory possibilities, which may improve the localization of epileptic networks.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".