Predictors of Surgical Candidacy in 414 Epilepsy Patients Admitted to the EMU
Bibliographic record
Abstract
OBJECTIVES: Admission to an Epilepsy Monitoring Unit (EMU) is essential for pre-surgical evaluation of patients with medically-refractory epilepsy; however, prolonged referral times and resource limitations are significant access barriers. Therefore, identification of pre-EMU variables that predict potential surgical candidates can assist in the triage of patient admissions to the EMU. METHODS: In this hypothesis-generating study, a retrospective analysis of patients admitted for pre-surgical evaluation to the Toronto Western Hospital EMU (2004-2011) was performed. Univariate and multivariate logistic regression was used to identify variables that could independently predict subsequent surgical candidacy following EMU evaluation. RESULTS: Four hundred and fourteen patients were admitted to the EMU. Overall, 259 patients (62.5%) were identified as potential surgical candidates. One hundred and seven patients (25.8%) required invasive electroencephalogram (iEEG) implantations; of 75 patients consenting to iEEG analysis 39 underwent a subsequent resective procedure. Male patients and those with a lesion on MRI were 1.9 times more likely to be surgical candidates (95% CI 1.18-2.98 and 0.94-3.80, respectively), while patients with non-localizable seizures were seven times less likely (95% CI 0.02-1.25). CONCLUSION: In this retrospective, hypothesis-generating study male gender, presence of a lesion on MRI and localizable seizures on routine outpatient EEG analysis independently predicted subsequent resective epilepsy surgical candidacy in EMU patients. Upon validation by other studies, these variables may be considered by clinicians referring patients to the EMU in order to improve wait times and optimize patient care.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".