Diagnosing functional neurological disorder: seeing the whole picture
Bibliographic record
Abstract
Functional neurological disorder (FND) is a complex neuropsychiatric syndrome with many phenotypes that are commonly encountered in clinical practice. Despite the heterogeneity of FND, the rate of misidentification is consistently low. For the more common motor subtypes, there are clear positive clinical, electrophysiological, and rarely imaging criteria that can establish the diagnosis in the traditional sense. For nonmotor subtypes, the characterization may be less clear. Here, we argue that the current diagnostic criteria are not reflective of the current shared neuropsychiatric understanding of FND, and, as a result, provide an incomplete picture of the diagnosis. We propose a three-step diagnostic triad for FND, in which the traditional neurological diagnosis is only the first element. Other steps include psychiatric/psychological formulation, integration, and follow-up. We advocate that this diagnostic approach should be the shared responsibility of neurology and mental health professionals. Finally, a research agenda is proposed to address the missing factors in the field.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".