How do I Know That the Jerks I See Are Tics?
Bibliographic record
Abstract
Tics are prevalent hyperkinesias that are most often encountered in the context of a primary tic disorder, as in Tourette syndrome. Although their recognition is typically straightforward, they often share some phenomenological features with other jerky hyperkinesias and may be mislabeled as such. These include myoclonic jerks, dystonia, chorea, stereotypies, as well as functional movement disorders. Here we discuss specific clues from clinical history and highlight relevant phenomenological qualities of tics, as well as their differences from other hyperkinetic disorders. We also showcase a broad range of relevant videos to facilitate correct recognition and labeling of motor phenomena. Our goal is to support clinicians in their diagnostic approach to tics, including their distinction from other jerky movement disorders. We believe that this will not only improve diagnostic accuracy in tic disorders, but it will also expedite appropriate care where needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".