Electroconvulsive therapy for self-injurious behaviour in autism spectrum disorders
Bibliographic record
Abstract
PURPOSE OF REVIEW: Self-injurious behaviour (SIB) is a devastating condition frequently encountered in autism spectrum disorders (ASDs) that can lead to dangerous tissue injury and profound psychosocial difficulty. An increasing number of reports over the past decade have demonstrated the swift and well tolerated resolution of intractable SIB with electroconvulsive therapy (ECT) when psychopharmacological and behavioural interventions are ineffective. The current article provides a review of the salient literature, including the conceptualization of repetitive self-injury along the catatonia spectrum, and further clarifies the critical distinction between ECT and contingent electric shock. RECENT FINDINGS: We searched electronically for literature regarding ECT for self-injurious behaviour from 1982 to present, as the first known report was published in 1982. Eleven reports were identified that presented ECT in the resolution of self-injury in autistic or intellectually disabled patients, and another five reports discussed such in typically developing individuals. These reports and related literature present such self-injury along the spectrum of agitated catatonia, with subsequent implications for ECT. SUMMARY: Intractable self-injury remains a significant challenge in ASDs, especially when patients do not respond adequately to behavioural and psychopharmacological interventions. ECT is well tolerated and efficacious treatment for catatonia, and can confer marked reduction in SIB along the agitated catatonia spectrum.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".