Severe self-injurious behaviours: A significant paediatric problem
Bibliographic record
Abstract
Mary is a 13-year-old girl who is autistic and has intellectual disability and attention-deficit hyperactivity disorder (ADHD). She engages in daily episodes of severe self-injurious behaviours (SIB), including self-biting and punching. She also struggles with significant anxiety and emotional dysregulation. These behaviours are often accompanied by aggression towards others and destructive outbursts. Mary attends a community school in a specialized classroom but has recently been asked to stop attending after injuring an educational assistant during an episode of severe dysregulation. She has also been restricted from participating in recreational programs due to safety concerns. Mary lives at home with her mother, who left her job to provide full-time care. The family has not received respite in over 2 years despite ongoing applications. Mary’s community paediatrician has trialled several medications, including stimulants and aripiprazole, but she continues to have severe SIB and associated behaviours of concern. Behavioural supports are limited in her region, and a referral to developmental paediatrics is still pending after 8 months. The family has also been referred to multiple allied health services, but most have long waitlists or no intake capacity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".