How technology is driving the landscape of epilepsy surgery
Bibliographic record
Abstract
This article emphasizes the role of the technological progress in changing the landscape of epilepsy surgery and provides a critical appraisal of robotic applications, laser interstitial thermal therapy, intraoperative imaging, wireless recording, new neuromodulation techniques, and high-intensity focused ultrasound. Specifically, (a) it relativizes the current hype in using robots for stereo-electroencephalography (SEEG) to increase the accuracy of depth electrode placement and save operating time; (b) discusses the drawback of laser interstitial thermal therapy (LITT) when it comes to the need for adequate histopathologic specimen and the fact that the concept of stereotactic disconnection is not new; (c) addresses the ratio between the benefits and expenditure of using intraoperative magnetic resonance imaging (MRI), that is, the high technical and personnel expertise needed that might restrict its use to centers with a high case load, including those unrelated to epilepsy; (d) soberly reviews the advantages, disadvantages, and future potentials of neuromodulation techniques with special emphasis on the differences between closed and open-loop systems; and (e) provides a critical outlook on the clinical implications of focused ultrasound, wireless recording, and multipurpose electrodes that are already on the horizon. This outlook shows that although current ultrasonic systems do have some limitations in delivering the acoustic energy, further advance of this technique may lead to novel treatment paradigms. Furthermore, it highlights that new data streams from multipurpose electrodes and wireless transmission of intracranial recordings will become available soon once some critical developments will be achieved such as electrode fidelity, data processing and storage, heat conduction as well as rechargeable technology. A better understanding of modern epilepsy surgery will help to demystify epilepsy surgery for the patients and the treating physicians and thereby reduce the surgical treatment gap.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".