A systematic review of the biopsychosocial dimensions affected by chronic pain in children and adolescents: identifying reliable and valid pediatric multidimensional chronic pain assessment tools
Bibliographic record
Abstract
Abstract Pediatric chronic pain is a complex experience that is often challenging to describe and measure. Multidimensional tools that evaluate the biopsychosocial impact of chronic pain in pediatric patients can help clinicians to prioritize and tailor interdisciplinary pain care; yet, the psychometric value and clinical utility of such tools has not yet been systematically studied in the literature. The purpose of this review was to identify multidimensional biopsychosocial tools used in pediatric chronic pain, synthesize their reliability and validity evidence, and draw on this evidence to describe the relationships between chronic pain and biopsychosocial domains. The search involved 2 phases to (1) identify eligible tools and (2) conduct a measured forward citation search of tool development articles. Tool eligibility was guided by the Multidimensional Biobehavioral Model of Pediatric Pain and study eligibility was focused on primary chronic pain diagnoses unrelated to disease. Data extraction was focused on reliability and validity evidence of eligible tools, guided by the Standards for Educational and Psychological Testing. Results yielded 6 tools that included 64 eligible studies, highlighting 84 significant relationships between pain and functional interference across 11 biopsychosocial variables. All tools were shown to have good internal consistency and evidence of validity, primarily through relationships to other variables. Of the 6 tools, the most brief and easy to use were the most under studied. Further psychometric research is warranted for these tools to investigate their clinical utility and psychometric properties in guiding and prioritizing pain care for children and adolescents.
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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 | 0.018 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".