Assessment of Aggressive Behaviour in a Patient with Autism Spectrum Disorder Requiring General Anesthesia.
Bibliographic record
Abstract
OBJECTIVE: To consider the utility of general anesthesia in the assessment of aggressive behaviour associated with autism spectrum disorder (ASD). METHODS: We describe the case of an adolescent male exhibiting violent behaviour with a previous diagnosis of ASD and review medical literature relevant to the assessment of aggression in the context of ASD. RESULTS: A 16-year-old male with a prior diagnosis of ASD, who was non-verbal, was admitted to an inpatient psychiatry ward with the presenting issue of violent behaviour. The patient had not received routine medical or dental care for several years due to agitation and aggression when attempts to physically examine him were made. General anesthesia was necessary to assess for medical conditions that may be contributory to his behavioural changes. While under general anesthesia, he was physically examined by several consulting services, received brain imaging, and laboratory specimens were drawn. CONCLUSIONS: Aggressive behaviour is a common issue for patients with ASD. When a patient's behaviour precludes examination and investigations, general anesthesia may be beneficial to facilitate the assessment process. This case illustrates the importance of a multidisciplinary approach in the assessment and management of a minimally verbal patient presenting with behavioural changes. To the knowledge of the authors, this represents the first published case report of a patient with ASD requiring general anesthesia for the assessment of aggressive behaviour.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 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".