An Overview of Psychogenic Non-Epileptic Seizures: Etiology, Diagnosis and Management
Bibliographic record
Abstract
The purpose of this review is to provide an update of the research regarding the etiology, diagnosis and management of psychogenic non-epileptic seizures (PNES). A literature search using Pubmed, Ovid MEDLINE and EMBASE database was performed from 2000 up to August 2017. We have evaluated the different factors leading to PNES as well as the diagnostic approach and management of this disorder which continue to be very difficult. The coexistence of epilepsy and PNES poses special challenges and requires the coordinated efforts of the family physicians, psychiatrists, psychologists and neurologists. Although this condition has an overall poor prognosis, a multidisciplinary approach in the diagnosis and management of this disorder would likely improve the outcomes. We have proposed a diagnostic and treatment algorithm for PNES and suggested a national registry of patients suffering from this condition. The registry would contain data regarding treatment and outcomes to aid in the understanding of this entity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".