How to do things with words: Two seminars on the naming of functional (psychogenic, non-epileptic, dissociative, conversion, …) seizures
Bibliographic record
Abstract
Amongst the most important conditions in the differential diagnosis of epilepsy is the one that manifests as paroxysms of altered behaviour, awareness, sensation or sense of bodily control in ways that often resemble epileptic seizures, but without the abnormal excessive or synchronous electrical activity in the brain that defines these. Despite this importance, there remains little agreement - and frequent debate - on what to call this condition, known inter alia as psychogenic non-epileptic seizures (PNES), dissociative seizures (DS), functional seizures (FS), non-epileptic attack disorder (NEAD), pseudoseizures, conversion disorder with seizures, and by many other labels besides. This choice of terminology is not merely academic - it affects patients' response to and understanding of their diagnosis, and their ability to navigate health care systems.This paper summarises two recent discussions hosted by the American Epilepsy Society and Functional Neurological Disorders Society on the naming of this condition. These discussions are conceptualised as the initial step of an exploration of whether it might be possible to build consensus for a new diagnostic label.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".