The spectrum of functional tic‐like behaviours: Data from an international registry
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Between 2019 and 2022, there was a marked rise in adolescents/young adults seeking urgent help for functional tic-like behaviours (FTLBs). Given the global scale of this phenomenon, we aimed to pool cases from different institutions in an international registry to better characterize this spectrum and facilitate future longitudinal observation. METHODS: An international collaborative group from 10 tertiary referral centres for tic disorders collected retrospective data on FTLB patients who sought specialists' attention between the last quarter of 2019 and June 2022. An audit procedure was used for collection of data, which comprised demographics, course of presentation and duration, precipitating and predisposing factors, phenomenology, comorbidities, and pharmacological treatment outcome. RESULTS: During the study period, we collected data on 294 patients with FTLBs, 97% of whom were adolescents and young adults and 87% of whom were female. FTLBs were found to have a peak of severity within 1 month in 70% of patients, with spontaneous remissions in 20%, and a very high frequency of complex movements (85%) and vocalizations (81%). Less than one-fifth of patients had pre-existing primary tic disorder, 66% had comorbid anxiety disorders, 28% comorbid depressive disorders, 24% autism spectrum disorder and 23% attention deficit/hyperactivity disorder. Almost 60% explicitly reported exposure to tic-related social media content. The vast majority of pharmacologically treated patients did not report benefit with tic-suppressing medications. CONCLUSIONS: Our data from the largest multicentre registry of FTLBs to date confirm substantial clinical differences from primary tic disorders. Social modelling was the most relevant contributing factor during the pandemic. Future longitudinal analyses from this database may help understand treatment approaches and responsiveness.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".