Pre-Surgical Evaluation and Seizure Outcome of Surgical Treatments for Pediatric Drug Resistant Epilepsy
Bibliographic record
Abstract
Approximately one-third of children with epilepsy have drug-resistant epilepsy (DRE), that is, poorly controlled seizures despite treatment with two or more appropriately selected anti-seizure medications. Although open resective surgery is recommended for DRE, there is variability in the reported seizure outcome. Further, it remains an underutilized treatment because of fear or misconceptions of the risk of surgery. A minimally invasive MRI laser interstitial thermal therapy (MRgLITT) has been suggested as an alternative to open surgery, but effectiveness compared to open surgery is unknown. There has also been reports of changing trends in pre-surgical evaluation and epilepsy surgery in Europe and the USA. It is unclear if such changes are observed in Canada. The dissertation will address these aims: (1) evaluate machine learning (ML) algorithms for predicting seizure outcome after open surgery and compare performance of ML to a classical statistical method; (2) compare seizure outcome, complications and length of hospitalization of MRgLITT to open epilepsy surgery (3) evaluate ML models for prediction of seizure outcome following MRgLITT and identify important predictors of seizure freedom and (4) assess trends of pre-surgical evaluation and epilepsy surgery at the Hospital for Sick Children and compare trends in the later period (2014-2019) to an earlier period (2001-2013). We found that ML algorithms performed well for prediction of seizure outcome following open surgery as well as MRgLITT, and performed better than the classical statistical method. Further, ML could identify important predictors of seizure freedom, which could assist with patient selection for these treatments. In the matched cohort of MRgLITT to open surgery patients, MRgLITT had lower seizure-free outcome than open surgery patients but had the advantage of better safety profile and shorter hospital stay. Such findings are important for pre-surgical counselling of families on the benefits and risks of MRgLITT. We found that despite an increasing number of pre-surgical evaluations, there was a reduction in the number of surgeries, particularly in the later period, due to a larger proportion of patients in whom the seizures could not be localized. Understanding the changing trends will help with decisions on allocating healthcare resources.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".