The prevalence of chronic pain in children and adolescents: a systematic review update and meta-analysis
Bibliographic record
Abstract
ABSTRACT: Chronic pain, defined as persistent or recurring pain or pain lasting longer than 3 months, is a common childhood problem. The objective of this study was to conduct an updated systematic review and meta-analysis on the prevalence of chronic pain (ie, overall, headache, abdominal pain, back pain, musculoskeletal pain, multisite/general pain, and other) in children and adolescents. EMBASE, PubMed, CINAHL, and PsycINFO were searched for publications between January 1, 2009, and June 30, 2023. Studies reporting population-based estimates of chronic nondisease related pain prevalence in children or adolescents (age ≤ 19 years) were included. Two independent reviewers screened articles based on a priori protocol. One hundred nineteen studies with a total of 1,043,878 children (52.0% female, mean age 13.4 years [SD 2.4]) were included. Seventy different countries were represented, with the highest number of data points of prevalence estimates coming from Finland and Germany (n = 19 each, 4.3%). The overall prevalence of chronic pain in children and adolescents was 20.8%, with the highest prevalence for headache and musculoskeletal pain (25.7%). Overall, and for all types of pain except for back pain and musculoskeletal pain, there were significant differences in the prevalence between boys and girls, with girls having a higher prevalence of pain. There was high heterogeneity (I 2 99.9%). Overall risk of bias was low to moderate. In summary, approximately 1 in 5 children and adolescents experience chronic pain and prevalence varies by pain type; for most types, there is higher pain prevalence among girls than among boys. Findings echo and expand upon the systematic review conducted in 2011.
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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 | 0.024 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".