Implantation of Stereoelectroencephalography Electrodes: A Systematic Review
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it