European Society for the Study of Tourette Syndrome 2022 criteria for clinical diagnosis of functional tic‐like behaviours: International consensus from experts in tic disorders
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In 2020, health professionals witnessed a dramatic increase in referrals of young people with rapid onset of severe tic-like behaviours. We assembled a working group to develop criteria for the clinical diagnosis of functional tic-like behaviours (FTLBs) to help neurologists, pediatricians, psychiatrists, and psychologists recognize and diagnose this condition. METHODS: We used a formal consensus development process, using a multiround, web-based Delphi survey. The survey was based on an in-person discussion at the European Society for the Study of Tourette Syndrome (ESSTS) meeting in Lausanne in June 2022. Members of an invited group with extensive clinical experience working with patients with Tourette syndrome and FTLBs discussed potential clinical criteria for diagnosis of FTLBs. An initial set of criteria were developed based on common clinical experiences and review of the literature on FTLBs and revised through iterative discussions, resulting in the survey items for voting. RESULTS: In total, 24 members of the working group were invited to participate in the Delphi process. We propose that there are three major criteria and two minor criteria to support the clinical diagnosis of FTLBs. A clinically definite diagnosis of FTLBs can be confirmed by the presence of all three major criteria. A clinically probable diagnosis of FTLBs can be confirmed by the presence of two major criteria and one minor criterion. CONCLUSIONS: Distinguishing FTLBs from primary tics is important due to the distinct treatment paths required for these two conditions. A limitation of the ESSTS 2022 criteria is that they lack prospective testing of their sensitivity and specificity.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".