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Record W3104655520 · doi:10.1017/s1092852920001996

Diagnosing functional neurological disorder: seeing the whole picture

2020· article· en· W3104655520 on OpenAlexaff
Sarah C. Lidstone, Walid Nassif, Jorge L. Juncos, Stewart A. Factor, Anthony E. Lang

Bibliographic record

VenueCNS Spectrums · 2020
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsNeurologyPsychiatryTriad (sociology)Clinical PracticePsychologyMedicineConversion disorderNeurosciencePhysical medicine and rehabilitationPhysical therapy

Abstract

fetched live from OpenAlex

Functional neurological disorder (FND) is a complex neuropsychiatric syndrome with many phenotypes that are commonly encountered in clinical practice. Despite the heterogeneity of FND, the rate of misidentification is consistently low. For the more common motor subtypes, there are clear positive clinical, electrophysiological, and rarely imaging criteria that can establish the diagnosis in the traditional sense. For nonmotor subtypes, the characterization may be less clear. Here, we argue that the current diagnostic criteria are not reflective of the current shared neuropsychiatric understanding of FND, and, as a result, provide an incomplete picture of the diagnosis. We propose a three-step diagnostic triad for FND, in which the traditional neurological diagnosis is only the first element. Other steps include psychiatric/psychological formulation, integration, and follow-up. We advocate that this diagnostic approach should be the shared responsibility of neurology and mental health professionals. Finally, a research agenda is proposed to address the missing factors in the field.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.240
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2020
Admission routes1
Has abstractyes

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