Functional Neurological Symptom Disorder: A Diagnostic Algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 5 th 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it