Stereotypies: A critical appraisal and suggestion of a clinically useful definition
Bibliographic record
Abstract
The foundations of the clinical classification of movement disorders rest on the precise definition of the words used to describe the disorders. Here we argue that the current use of the term stereotypy falls well short of the precision needed for either clinical or academic use, and fails both to provide a clinically useful diagnostic category and to define a set of conditions that are linked pathophysiologically. The difficulty in defining this concept is not a new one as our review of the history of the term demonstrates. We synthesise this historical background, explore why clinicians have felt it necessary to use the category of stereotypy for certain movements rather than the related category of tics, discuss the multiple uses of the term in current research and clinical practice and on this basis suggest a new definition and classification.
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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 | 0.089 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.019 | 0.009 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.007 | 0.013 |
| 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".