Suicide in Autism Spectrum Disorders
Bibliographic record
Abstract
This review focuses on suicide in patients with Autism Spectrum Disorders (ASD) as well as risk factors and comorbidities of persons with ASD who have attempted suicide. Research was conducted by searching PubMed and Psychinfo for articles. Suicide in ASD is largely understudied. Although suicide is common in clinical samples, we have little knowledge of suicide in persons with ASD in the general population. Comorbidity, particularly with depression and other affective disorders or schizoid disorders and psychotic symptoms, is often reported, so it is difficult to determine if suicidality is associated with ASD or the comorbid disorder. Clinical samples suggest that suicide occurs more frequently in high functioning autism. Physical and sexual abuse, bullying, and changes in routine are precipitating events associated with suicide risk. Persons with ASD present risk factors inherent to their diagnosis (deficit in expression of feelings and thoughts), along with risk factors pertaining to the general population (abuse, depression, anxiety, etc.). The inability of persons with Pervasive Developmental Disorder (PDD) to express emotions and thoughts makes the diagnosis of suicidal ideation difficult and demands important adjustments to traditional psychotherapeutic interventions. More research is needed to determine the incidence of suicidal behaviors in persons with ASD, to identify risk and protective factors, as well as to assess the effectiveness of prevention strategies and interventions.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".