Self-injurious behaviour in movement disorders: systematic review
Bibliographic record
Abstract
Self-injurious behaviours (SIBs) are defined as deliberate, repetitive and persistent behaviours that are directed towards the body and lead to physical injury and are not associated with sexual arousal and without suicidal intent. In movement disorders, SIBs are typically associated with tic disorders, most commonly Tourette syndrome, and neurometabolic conditions, such as classic Lesch-Nyhan syndrome. However, beyond these well-known aetiologies, a range of other movement disorder syndromes may also present with SIBs, even though this clinical association remains less well-known. Given the scarcity of comprehensive works on this topic, here we performed a systematic review of the literature to delineate the spectrum of movement disorder aetiologies associated with SIBs. We report distinct aetiologies, which are clustered in five different categorical domains, namely, neurodevelopmental, neurometabolic and neurodegenerative disorders, as well as disorders with characteristic structural brain changes and heterogeneous aetiologies (eg, autoimmune and drug-induced). We also provide insights in the pathophysiology of SIBs in these patients and discuss neurobiological key risk factors, which may facilitate their manifestation. Finally, we provide a list of treatments, including practical measures, such as protective devices, as well as behavioural interventions and pharmacological and neurosurgical therapies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".