Testing an online screening tool for epilepsy surgery evaluation
Bibliographic record
Abstract
BACKGROUND: Epilepsy surgery is recognized for its effectiveness in controlling seizures in a significant number of patients with drug-resistant epilepsy. Despite this, there remains a notable deficiency in referring these patients for video-electroencephalogram (EEG) monitoring and surgical evaluation. Addressing this gap, the Canadian Appropriateness of Epilepsy Surgery (CASES), an online tool for epilepsy surgery evaluation (www.epilepsycases.com), was developed to aid physicians in the referral process of patients with refractory epilepsy to surgical assessments. OBJECTIVE: The present study aimed to evaluate the utility of CASES in identifying candidates for epilepsy surgery, thereby facilitating clinical decision-making for patients with drug-resistant epilepsy. METHODS: A cross-sectional analysis was conducted using the CASES platform to assess surgical candidacy among individuals with epilepsy. Participants were selected among those receiving care at the Epilepsy Clinic of the Neurology Service, Hospital de Clínicas de Porto Alegre, Brazil, over a 3-month period. The study cohort included 211 patients. Data were systematically extracted from patient medical records or collected during clinical appointments. RESULTS: Of the evaluated cohort, 59.6% were identified as potential candidates for video-EEG monitoring and subsequent surgical evaluation. Factors significantly associated with recommendations for video-EEG and surgical assessment included seizure frequency, the number of antiseizure medications (ASMs) trialed, and the occurrence of drug-related adverse effects. CONCLUSION: The CASES showed significant potential in guiding recommendations for video-EEG monitoring and facilitating referrals for epilepsy surgery. This tool may not only enhance patient treatments and outcomes but also contribute to cost savings in epilepsy management in both the short and long term.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".