Do We Practice What We Preach? Examining Barriers to Timely Epilepsy Surgery Referrals
Bibliographic record
Abstract
Surveying neurologist perspectives and knowledge of epilepsy surgery to identify barriers to surgery referral Namal U. Seneviratne Objective: Epilepsy surgery is an effective means of treating medically refractory epilepsy (MRE), but it remains underused. We aimed to analyze the perspectives and knowledge of referring neurologists in the New York metropolitan area, who serve a large epilepsy population. Methods: We adapted a previous Canadian survey by Roberts et al, adding questions regarding demographic descriptors, insurance coverage, training and practice details, and perceived social barriers for patients. We surveyed neurologists directly affiliated with Montefiore Medical Center and those referring to Montefiore's Comprehensive Epilepsy Center. Participants had 10 weeks to fill out an online Qualtrics survey with weekly reminders. Results: Of 117 neurologists contacted, 51 eligible neurologists completed the survey (63.8% Montefiore, 35.0% referring group). A high proportion of the results were from epilepsy-trained individuals (41.2%) and neurologists who graduated residency ≤19 years ago (80.4%). About 80.4% of respondents felt that epilepsy surgery is safe, but only 56.9% would refer a patient for surgical workup after two failed trials of antiseizure medications. Epileptologists and providers with a larger volume of epilepsy patients and electroencephalogram readings had better knowledge of the epilepsy surgery workup guidelines. When asked to rank social barriers to patients receiving surgery, participants were most concerned about lack of social support, financial insecurity, and a patient's dual role as a caregiver. Significance: Our study suggests continued reluctance of neurologists regarding epilepsy surgery and deficiencies in the knowledge and adherence to the recommended guidelines. In the context of prior studies, these results showed and improved understanding of the definition of MRE (80.4%) and an increased likelihood to refer eligible patients as early as possible (78.4%) in line with current consensus recommendations. The finding that epilepsy-trained and more epilepsy/electroencephalogram-facing neurologists showed a better understanding of the guidelines suggests that the increased education efforts should be targeted at nonepileptologists.
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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 | 0.011 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".