Chronic Pain in Autism
Bibliographic record
Abstract
OBJECTIVES: The aim of this systematic review was to synthesize the current research on chronic pain in autistic individuals, including epidemiology, assessment, and management. METHODS: We conducted a search of the following electronic databases: PubMed/MedLine, CINHAL, PsychINFO, PubPsych, Scopus, and Web of Science, from inception to 31 July 2024. RESULTS: A total of 5603 citations were identified, 87 articles were deemed eligible for further assessment, and 26 articles were included in the final review. Of these, 13 provided data about the epidemiology of chronic pain, 10 were related to pain assessment, and 3 focused on chronic pain treatment. The most commonly studied locations of chronic pain were the abdomen and the head. The assessment tools used were frequently completed by parents or professionals/researchers and only 1 study used self-reported measures. Three studies were on psychological interventions applied to the management of chronic pain in autistic individuals. DISCUSSION: The results of this study provides initial insights into chronic pain in autistic individuals, and show that they experience conditions such as chronic abdominal pain and migraines as the general population does. It also highlights challenges to the accurate assessment and treatment of chronic pain, and emphasizes the need for heightened clinician awareness, early identification, and personalized management strategies.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".