Access to diagnostic and therapeutic facilities for psychogenic nonepileptic seizures: An international survey by the <scp>ILAE PNES</scp> Task Force
Bibliographic record
Abstract
OBJECTIVE: Studies from a small number of countries suggest that patients with psychogenic nonepileptic seizures (PNES) have limited access to diagnostic and treatment services. The PNES Task Force of the International League Against Epilepsy (ILAE) carried out 2 surveys to explore the diagnosis and treatment of PNES around the world. METHODS: A short survey (8 questions) was sent to all 114 chapters of the ILAE. A longer survey (36 questions) was completed by healthcare professionals who see patients with seizures. Questions were separated into 5 sections: professional role, diagnostic methods, management, etiology, and access to health care. RESULTS: Responses were received from 63 different countries. The short survey was completed by 48 ILAE chapters, and the long survey by 1098 health professionals from 28 countries. PNES were recognized as a diagnostic and therapeutic problem in all countries. Trauma and mental health issues were most commonly recognized as etiologic factors. There was a clear relationship between income and access to diagnostic tests and expertise. Psychological therapy was most commonly considered the treatment of choice. Although financial difficulties were the most commonly reported problem with service access in low-income countries, in all countries stigma, lack of popular awareness, and lack of information posed challenges. SIGNIFICANCE: This global provider survey demonstrates that PNES are a health problem around the world. Health care for PNES could be improved with better education of healthcare professionals, the development of reliable and simple diagnostic procedures that do not rely on costly tests, and the provision of accessible information.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".