Functional Neurological Symptom Disorder: A Diagnostic Algorithm
Bibliographic record
Abstract
Functional neurological symptom disorder (FNSD) is a neuropsychiatric disorder characterized by the presence of neurological symptoms in the absence of any neurological abnormality that can be linked to a known pathology. Few studies have taken interest in this subject probably because of the heterogeneity of results. It is most often a diagnosis of exclusion which often means that patients undergo many tests and find themselves erring for a diagnosis with very little satisfaction of the outcomes. A reliable imagery pattern would therefore provide some relief and confirmation for both patients and clinicians. It could also facilitate acceptation of the diagnosis and reduce the societal cost associated with FNSD for the patient. The aim of this present study was to describe a clinicoradiological correspondence algorithm of FNSD using the PET scan and SPECT scan (PoSPs) and grant the clinician with a reliable tool to facilitate the diagnosis of FNSD. A systematic review according to the 2009 PRISMA criteria statement was used to guide the review. Our study included 3 of our own consenting patients who met the Diagnostic and Statistical Manual of Mental Disorders 5th edition criteria as well as 25 other patients from 7 different studies. Our results showed a hypoactivation with poor clinicoradiological correspondence and poor stability in time. This hypoactivation was mostly in the frontal lobe, which could explain some behavioral alterations. These findings oppose the ones found in organic pathologies and therefore should orient towards FNSD. In the light of these findings, we recommend the clinicians to perform two PoSPs, searching for clinicoradiological lack of correspondence and time stability using our algorithm.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".