Prevalence of and recommendation for measuring chronic postsurgical pain in children: an updated systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: ), approximately 20% of children and adolescents develop chronic postsurgical pain (CPSP; ie, pain persisting >3 months after surgery) after major surgeries, which is associated with adverse functional and psychological consequences. A major barrier was that definitions of CPSP applied were highly variable. Since that prior review was conducted (n=4 studies in meta-analysis), numerous relevant studies have been published warranting an update. OBJECTIVE: The aims of this current review were to: (1) provide an updated prevalence estimate for pediatric CPSP and (2) examine definitions of pediatric CPSP applied in current research. EVIDENCE REVIEW: Prospective, observational studies examining CPSP using a validated self-report pain intensity measure in children were included. 4884 unique publications were screened with 20 articles meeting inclusion criteria. Risk of bias using Quality in Prognostic Study tool ranged from low to high. FINDINGS: The pooled prevalence of CPSP among mostly major surgeries was 28.2% (95% CI 21.4% to 36.1%). Subgroup analysis of spinal fusion surgeries identified a prevalence of 31% (95% CI 21.4% to 43.5%). Using Grading of Recommendations, Assessment, Development, and Evaluation, the certainty in prevalence estimates was moderate. Studies used a range of valid pain intensity measures to classify CPSP (eg, Numeric Rating Scale), often without pain interference or quality of life measures. CONCLUSIONS: The overall prevalence of pediatric CPSP is higher than estimated in the prior review, and quality of studies generally improved though with some heterogeneity. Standardizing the measurement of CPSP will facilitate future efforts to combine and compare data across studies. PROSPERO REGISTRATION NUMBER: CRD42022306340.
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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 | 0.031 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".