Current Concepts in Diagnosis and Treatment of Functional Neurological Disorders
Bibliographic record
Abstract
Importance: Functional neurological disorders (FND) are common sources of disability in medicine. Patients have often been misdiagnosed, correctly diagnosed after lengthy delays, and/or subjected to poorly delivered diagnoses that prevent diagnostic understanding and lead to inappropriate treatments, iatrogenic harm, unnecessary and costly evaluations, and poor outcomes. Observations: Functional Neurological Symptom Disorder/Conversion Disorder was adopted by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, replacing the term psychogenic with functional and removing the criterion of psychological stress as a prerequisite for FND. A diagnosis can now be made in an inclusionary manner by identifying neurological signs that are specific to FNDs without reliance on presence or absence of psychological stressors or suggestive historical clues. The new model highlights a wider range of past sensitizing events, such as physical trauma, medical illness, or physiological/psychophysiological events. In this model, strong ideas and expectations about these events correlate with abnormal predictions of sensory data and body-focused attention. Neurobiological abnormalities include hypoactivation of the supplementary motor area and relative disconnection with areas that select or inhibit movements and are associated with a sense of agency. Promising evidence has accumulated for the benefit of specific physical rehabilitation and psychological interventions alone or in combination, but clinical trial evidence remains limited. Conclusions and Relevance: Functional neurological disorders are a neglected but potentially reversible source of disability. Further research is needed to determine the dose and duration of various interventions, the value of combination treatments and multidisciplinary therapy, and the therapeutic modality best suited for each patient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".