Treatments for Functional Neurological Disorder: A Practical Guide for Program Development
Bibliographic record
Abstract
Functional neurological disorder (FND) is an often-disabling condition with a complex path to diagnosis, further challenged by limited availability of evidence-based treatment resources. Providers hoping to offer treatment resources face the challenge of identifying effective and sustainable implementation of interventions. The existing literature provides limited guidance on the logistics of creating specialized programs for FND outside of tertiary care centers or high-resource medical settings. Members of a multidisciplinary treatment team may find it challenging to identify program development resources that provide a unified perspective on each member's role and how they function together. The authors' FND program at the Stanford University School of Medicine has recently fielded a high number of requests by clinicians, health care staff, and administrators across the United States for collaboration to start new programs. Frequently asked questions include the criteria for patient selection, what personnel to include, how to ensure prompt staff responses to FND symptoms, when to hospitalize patients, how to obtain funding for services, and more. The intended audience for this review includes seasoned and new clinicians, allied health professionals, and nonclinicians, including administrators. The authors discuss diagnosis and evidence-based treatment strategies and provide guidance on practical issues, including work, disability, and driving. The authors' program experience is highlighted as an example, and alternative working models are discussed. The aim of this article is to improve providers' knowledge and confidence and remove frequently encountered barriers to program development. The authors seek to provide a resource that may help connect those in need of care to FND services.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.063 | 0.040 |
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".