Response: Can biomarkers of the epileptogenic zone be characterized in patients rendered seizure free alone?
Bibliographic record
Abstract
The role of presurgical evaluation with intracranial electroencephalography (iEEG) is to localize the minimal amount of brain tissue which, if resected, will achieve seizure freedom in the treated patient. From the definition of the epileptogenic zone (EZ)1 being “the area of cortex that is indispensable for the generation of epileptic seizures,” it is the EZ that we want to localize. At the same time we will only know retrospectively in patients with seizure-free outcome that we were indeed able to identify the EZ.2 This brings us to the localization dilemma. We either attempt to stick to the primary goal of localization of the EZ and use only good outcome patients or use something else as a localization target that is well defined in both good and poor outcomes. For poor outcome patients the latter remains less clear apart from the fact that at least part of the predicted target, localized by an algorithm, should be outside of the resection cavity. Another issue worth mentioning is that only patients with certain pathologies will reach a high likelihood of becoming seizure-free. This is another issue that can be a hurdle when developing machine/deep learning models that might indeed also identify traits of certain pathologies more likely in good outcome patients. We agree that determining the localization target is one of many problems when evaluating the pre-surgical iEEG. Having a unified strategy for the development of new biomarkers or building new machine/deep learning models for presurgical evaluation of iEEG would be very beneficial for the community. We are open to cooperate on such a strategy and to help to raise this problem in the community. Neither of the authors has any conflict of interest to disclose.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.039 | 0.015 |
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".