Distinguishing functional from primary tics: a study of expert video assessments
Bibliographic record
Abstract
BACKGROUND: Reliably applied criteria to differentiate functional from primary tics are lacking. In the absence of biological markers, the development of new diagnostic criteria to assist clinicians is predicated on expert judgement and consensus. This study examines the level of diagnostic agreement of experts in tic disorders using video footage and clinical descriptions. METHODS: Using a two-part survey, eight experts in the diagnosis and management of tics were first asked to study 24 case videos of adults with primary tics, functional tics or both and to select a corresponding diagnosis. In the second part of the survey, additional clinical information was provided, and the diagnosis was then reconsidered. Inter-rater agreement was measured using Fleiss' kappa. In both study parts, the factors which influenced diagnostic decision-making and overall diagnostic confidence were reviewed. RESULTS: Based on phenomenology alone, the diagnostic agreement among the expert raters was only fair for the pooled diagnoses (κ=0.21) as well as specifically for functional (κ=0.26) and primary tics (κ=0.24). Additional clinical information increased overall diagnostic agreement to moderate (κ=0.51) for both functional (κ=0.6) and primary tics (κ=0.57). The main factors informing diagnosis were tic semiology, age at tic onset, presence of premonitory urges, tic suppressibility, the temporal latency between tic onset and peak severity, precipitants and tic triggers and changes in the overall phenotypic presentation. CONCLUSIONS: This study confirmed that in the absence of clinical information, the diagnostic distinction between primary and functional tics is often difficult, even for expert clinicians.
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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 | 0.026 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".