The first-line management of psychogenic non-epileptic seizures (PNES) in adults in the emergency: a practical approach
Bibliographic record
Abstract
Abstract Distinguishing non-epileptic events, especially psychogenic non-epileptic seizures (PNES), from epileptic seizures (ES) constitutes a diagnostic challenge. Misdiagnoses are frequent, especially when video-EEG recording, the gold-standard for PNES confirmation, cannot be completed. The issue is further complicated in cases of combined PNES with ES. In emergency units, a misdiagnosis can lead to extreme antiepileptic drug escalade, unnecessary resuscitation measures (intubation, catheterization, etc.), as well as needless biologic and imaging investigations. Outside of the acute window, an incorrect diagnosis can lead to prolonged hospitalization or increase of unhelpful antiepileptic drug therapy. Early recognition is thus desirable to initiate adequate treatment and improve prognosis. Considering experience-based strategies and a thorough review of the literature, we aimed to present the main clinical clues for physicians facing PNES in non-specialized units, before management is transferred to epileptologists and neuropsychiatrists. In such conditions, patient recall or witness-report provide the first orientation for the diagnosis, recognizing that collected information may be inaccurate. Thorough analysis of an event (live or based on home-video) may lead to a clinical diagnosis of PNES with a high confidence level. Indeed, a fluctuating course, crying with gestures of frustration, pelvic thrusting, eye closure during the episode, and the absence of postictal confusion and/or amnesia are highly suggestive of PNES. Moreover, induction and/or inhibition tests of PNES have a good diagnostic value when positive. Prolactinemia may also be a useful biomarker to distinguish PNES from epileptic seizures, especially following bilateral tonic-clonic seizures. Finally, regardless the level of certainty in the diagnosis of the PNES, it is important to subsequently refer the patient for epileptological and neuropsychiatric follow-up.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".