Exploring the complexity of self-injurious behavior within autism spectrum disorder: a systematic review
Bibliographic record
Abstract
Introduction. Autism Spectrum Disorder (ASD) is one of the neurodevelopmental disorders characterized by communication deficits, repetitive behaviors, restricted interests, and difficulties in social interaction. In addition to these characteristics, some individuals with ASD also exhibit self-injurious behaviors (SIB), which can occur in different degrees of severity and are a relevant concern for parents and caregivers. Objective. To explore the relationship between self-injurious behavior in individuals with ASD, performing a systematic review based on established inclusion and exclusion criteria. Articles that evaluated self-injurious behaviors in individuals with ASD were analyzed, covering different age groups and geographic contexts. Method. Out of the 823 articles identified, six met the inclusion criteria, with studies conducted in the United Kingdom, the United States, Canada, and France. Results. Research has revealed that SIB is common in individuals with ASD, and its prevalence is observed in both children and adults. Factors such as cognitive abilities, the presence of painful conditions, and behavioral control were associated with the severity of self-injurious behaviors. No significant differences were found between sex and SIB, and pain sensitivity appears to vary between individuals. Conclusion. SIB is common in ASD, highlighting the importance of early interventions and therapeutic approaches to reduce long-term impacts and improve quality of life.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".