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Record W4283818750 · doi:10.1097/wco.0000000000001074

The recent surge of functional movement disorders: social distress or greater awareness?

2022· review· en· W4283818750 on OpenAlexaff
Christelle Nilles, Tamara Pringsheim, Davide Martino

Bibliographic record

VenueCurrent Opinion in Neurology · 2022
Typereview
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsAnxietyPsychopathologyDistressDepression (economics)PandemicMedicinePsychiatryVulnerability (computing)Mental healthPsychologyClinical psychologyCoronavirus disease 2019 (COVID-19)Disease

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To analyze the interrelated factors that have led to the striking increase in functional movement disorders in recent years, with a focus on functional tic-like behaviours (FTLB). RECENT FINDINGS: The sudden onset of FTLB has been widely observed in several countries since the beginning of the SARS-CoV-2 pandemic, whereas it was previously very rarely reported. Pandemic-related FTLB typically occur in young females, share complex, disabling and tic-lookalike patterns, and are usually triggered by the exposure to videos portraying tic-like behaviours on social media. Both pandemic-related FTLB and prepandemic FTLB are associated with high levels of depression and anxiety. FTLB related to the pandemic highlight the role of social media in the psychopathological behaviours of our time. SUMMARY: We suggest FTLB are due to a combination of predisposing factors (high genetic and epigenetic risk for anxiety and depression, negative past experiences) and precipitating factors (pandemic and its impact on mental health, exposure to certain social media content). These factors of vulnerability may increase the risk of developing behavioural and emotional problems in youth, such as FTLB. Early diagnosis and appropriate treatment of psychiatric comorbidities seem to be predictors of positive prognosis.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.195
GPT teacher head0.412
Teacher spread0.217 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations28
Published2022
Admission routes1
Has abstractyes

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