The recent surge of functional movement disorders: social distress or greater awareness?
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".