Post‐traumatic stress symptoms in children and adolescents with chronic pain: A topical review of the literature and a proposed framework for future research
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The co-occurrence of chronic pain and post-traumatic stress symptoms (PTSS) and post-traumatic stress disorder (PTSD) has gained increasing research attention. Studies on associations among pain and PTSS or PTSD in youth have largely been conducted in the context of acute injury or trauma. Less is known about the risk for co-occurrence with paediatric chronic pain. In this review, we (1) propose a conceptual framework to outline factors salient during childhood that may be associated with symptom severity, co-occurrence and mutual maintenance, (2) present relevant literature on PTSS in youth with acute and chronic pain and identify research gaps and (3) provide recommendations to guide paediatric research examining shared symptomatology. DATABASES AND DATA TREATMENT: Electronic databases (PubMed and Google Scholar) were used to identify relevant articles using the search terms 'child, adolescent, paediatric, chronic pain, acute pain, post-traumatic stress symptoms and post-traumatic stress disorder'. Studies were retrieved and reviewed based on relevance to the topic. RESULTS: Our findings revealed that existing biobehavioural and ecological models of paediatric chronic pain lack attention to traumatic events or the potential development of PTSS. Paediatric studies are also limited by lack of a conceptual framework for understanding the prevalence, risk and trajectories of PTSS in youth with chronic pain. CONCLUSIONS: Our new developmentally informed framework highlights individual symptoms and shared contextual factors that are important when examining potential associations among paediatric chronic pain and PTSS. Future studies should consider bidirectional and mutually maintaining associations, which will be aided by prospective, longitudinal designs. WHAT DOES THIS REVIEW ADD?: This review presents relevant literature on pain and PTSS in youth and proposes a conceptual framework to examine factors salient during childhood that may be associated with symptom severity, comorbidity and mutual maintenance of chronic pain and PTSS in paediatric populations. We highlight dynamic factors that may change across children's development and provide recommendations to guide paediatric research examining potential associations among PTSS and chronic pain.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| 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".