Driving a motor vehicle and psychogenic nonepileptic seizures: ILAE Report by the Task Force on Psychogenic Nonepileptic Seizures
Bibliographic record
Abstract
OBJECTIVES: This International League Against Epilepsy (ILAE) Report: (a) summarizes the literature about "driving and psychogenic nonepileptic seizures (PNES)"; (b) presents the views of international experts; and (c) proposes an approach to assessing the ability of persons with PNES (PwPNES) to drive. METHODS: Phase 1: Systematic literature review. Phase 2: Collection of international expert opinion using SurveyMonkey®. Experts included the members of the ILAE PNES Task Force and individuals with relevant publications since 2000. Phase 3: Joint analysis of the findings and refinement of conclusions by all participants using email. As an ILAE Report, the resulting text was reviewed by the Psychiatry Commission, the ILAE Task Force on Driving Guidelines, and Executive Committee. RESULTS: Eight studies identified by the systematic review process failed to provide a firm evidence base for PNES-related driving regulations, but suggest that most health professionals think restrictions are appropriate. Twenty-six experts responded to the survey. Most held the view that decisions about driving privileges should consider individual patient and PNES characteristics and take account of whether permits are sought for private or commercial driving. Most felt that those with active PNES should not be allowed to drive unless certain criteria were met and that PNES should be thought of as "active" if the last psychogenic seizure had occurred within 6 months. SIGNIFICANCE: Recommendations on whether PwPNES can drive should be made at the individual patient level. Until future research has determined the risk of accidents in PwPNES a proposed algorithm may guide decisions about driving advice.
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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 | 0.093 | 0.209 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| 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".