Implantation of Stereoelectroencephalography Electrodes: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Stereoelectroencephalography (SEEG) was developed by Talairach and Bancaud in Paris in the late 1950s. Subsequently, the Talairach methodology was adopted at a number of additional centers in Europe and Canada. Technical aspects remained essentially unchanged for the following 30 years. Only in the last two decades, because of advancements in image-guided surgery systems, robotics, and computer-aided planning, use of SEEG has become more widespread, and reports describing these new developments have been published. OBJECTIVES: This systematic review was designed to assess published reports of SEEG surgical techniques and safety profile. DATA SOURCES: An electronic search was performed of Medline, Embase, and Scopus databases. In addition, the content pages of several standard epilepsy surgery textbooks were searched. Full-text English studies describing SEEG surgical technique or pertinent epidemiological data were included. Conference abstracts, reviews, posters, editorials, comments, and letters were excluded. RESULTS: Three hundred fifty-nine of 2,903 potentially eligible studies published by 32 centers were reviewed. Thirty-one of these primarily discussed the surgical technique. Thirty-five major complications (including 4 fatalities) were reported among 4,000 patients (0.8%) implanted with 33,000 electrodes. LIMITATIONS: The number of SEEG patients is likely to be underestimated because only a few groups have exhaustively reported their experience. Moreover, it is possible that a number of teams performing SEEG have not published studies on the topic. CONCLUSIONS: Rigorous SEEG, thanks to its basic principles and updated technologies, is a safe and accurate method to define the epileptogenic zone by means of stereotactically implanted intracerebral electrodes.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".