Functional Neurological Disorder: Historical Trends and Urgent Directions
Bibliographic record
Abstract
The objective was to identify the gaps in understanding and management of functional neurological disorders (FNDs) that could be negatively impacting its incidence, prevalence, prognosis, and preventive tools. A narrative review was performed to synthetize evidence from multiple fields including genetic, epidemiological, functional neuroimaging and clinical studies, paying close attention to FND historical trends and recurring themes in nomenclature, classification, epidemiology, therapeutic tools, outcomes, prognosis, and pathophysiology. References included in this review were sourced from PubMed, covering January 1, 2000 to June 30, 2022, and from the references of relevant articles. Multiple problems associated with the current status of approach and management of FNDs were identified, including six major knowledge gaps. To overcome such shortfalls, we recommend the collaborative creation of a multi-network management algorithm that integrates all pathophysiological mechanisms involved in FND onset and perpetuation. It is hoped that an integrative model will facilitate the development of a biographically focused, biopsychosocial-spiritual management and preventive protocol, which incorporates key concepts and skills from the fields of neurology, psychiatry, psychology, and physiotherapy. Such comprehensive and concise protocol could be distributed through upskill programs across several medical specialties. Multidisciplinary collaboration is needed to fill current knowledge gaps, with multispecialty teams helping to overcome the deficits in outcomes and prognosis still affecting FND, one of the commonest and most expensive neurological disorders currently affecting humankind. J Neurol Res. 2023;13(1):12-32 doi: https://doi.org/10.14740/jnr754
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".