Novel tonometer device distinguishes brain stiffness in epilepsy surgery
Bibliographic record
Abstract
Complete surgical resection of abnormal brain tissue is the most important predictor of seizure freedom following surgery for cortical dysplasia. While lesional tissue is often visually indiscernible from normal brain, anecdotally, it is subjectively stiffer. We report the first experience of the use of a digital tonometer to understand the biomechanical properties of epilepsy tissue and to guide the conduct of epilepsy surgery. Consecutive epilepsy surgery patients (n = 24) from UCLA Mattel Children's Hospital were recruited to undergo intraoperative brain tonometry at the time of open craniotomy for epilepsy surgery. Brain stiffness measurements were corrected with abnormalities on neuroimaging and histopathology using mixed-effects multivariable linear regression. We collected 249 measurements across 30 operations involving 24 patients through the pediatric epilepsy surgery program at UCLA Mattel Children's Hospital. On multivariable mixed-effects regression, brain stiffness was significantly associated with the presence of MRI lesion (β = 32.3, 95%CI 16.3-48.2; p < 0.001), severity of cortical disorganization (β = 19.8, 95%CI 9.4-30.2; p = 0.001), and recent subdural grid implantation (β = 42.8, 95%CI 11.8-73.8; p = 0.009). Brain tonometry offers the potential of real-time intraoperative feedback to identify abnormal brain tissue with millimeter spatial resolution. We present the first experience with this novel intraoperative tool for the conduct of epilepsy surgery. A carefully designed prospective study is required to elucidate whether the clinical application of brain tonometry during resective procedures could guide the area of resection and improve seizure outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".