Systematic review of autistic representation in the treatment literature for pediatric chronic pain
Bibliographic record
Abstract
Chronic pain disproportionately affects autistic children and young people, yet they are underrepresented in pain research. Research on psychological, physical, and pharmacological therapies for other conditions suggests modifications are required to ensure treatment accessibility and efficacy for autistic individuals. However, no such evidence base has been synthesized in pediatric pain. The aim of this review was to (1) review existing "gold-standard" treatment literature for pediatric chronic pain to determine the representation of autistic participants, and (2) review literature on treatment of chronic pain specifically in autistic children and young people to describe the current evidence landscape and identify next directions for research. 16.7% (12/72) of randomized controlled trials included in Cochrane reviews of interventions for pediatric chronic pain explicitly excluded youth with a developmental delay/disability, of which only 8.3% specifically named autism. However, 52.8% of Cochrane-included trials had criteria or protocols which may have disproportionately impacted autistic participants, such as excluding intellectual disability, psychiatric conditions, medical conditions, and/or requiring participants to communicate verbally. Twenty-nine studies of treating chronic pain in autistic children and young people were identified, of which the majority were case reports (k = 27, 93%) with large variation in pain condition, intervention applied, and outcomes measured. Given the high prevalence of chronic pain in autistic children and young people, there is an ethical imperative to ensure their representation in intervention trials, co-develop interventions that address the specific needs of autistic individuals who live with pediatric chronic pain, and to increase accessibility in chronic pain research more broadly. REGISTRATION: PROSPERO: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=491423 registered March 19 2024. Open Science Framework: https://osf.io/8na64/ registered December 18, 2023 PERSPECTIVE: Autistic children and young people (CYP) are not represented in reviews of chronic pain treatments, and the literature on treating chronic pain specifically in this population is so variable no clear conclusions can be drawn. Efforts to increase accessibility of chronic pain interventions and research for autistic CYP is needed.
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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 | 0.020 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".