Lost in Transition: The Long and Winding Road Toward Epilepsy Surgery—An Analysis of Obstacles Prior to Surgery and Call for Orchestrated Health Care Efforts in Epilepsy
Bibliographic record
Abstract
Abstract Difficult-to-treat epilepsy is defined as ongoing seizures despite adequate pharmacological treatment. This condition is affecting a significant percentage of epilepsy patients and is estimated to be as high as one-third of all patients. Epilepsy surgery, targeting the removal of the key parts of cerebral convolutions responsible for seizure generation and often including a structural lesion, can be a very successful approach. However, this necessitates careful patient selection by comprehensive investigations, proving the localization of the epileptogenic zone as well as measures to make such surgeries safe. With careful selection as a prerequisite, the percentage of patients achieving seizure freedom by neurosurgical intervention is high, approximating two-thirds of all epilepsy surgeries performed. In contrast, the average duration of a patient's pharmacoresistant focal epilepsy prior to surgery anywhere around the globe is around 20 years. Given that typical patients are ∼30 to 40 years of age at the time of surgery, many patients have been living with chronic seizures since childhood or adolescence. This means that most of these patients have been going through several stages of medical care for years or even decades, both as children and adults, without ever being fully investigated and/or selected for surgery which is concerning. Yet, there is no set standard for a timeline leading toward successful surgery in epilepsy. It is obvious that the average transit period from the moment of first seizure manifestation until the day of successful surgery takes much too long. This is the reason why we see these patients lost in transition.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".