Chronic Postsurgical Pain in Children and Adolescents: A Call for Action
Bibliographic record
Abstract
Chronic postsurgical pain (CPSP) affects a significant proportion of children and adolescents after major surgery and is a detriment to both short- and long-term recovery outcomes. While clinical characteristics and psychosocial risk factors for developing CPSP in children and adults are well established in the literature, there has been little progress on the prevention and management of CPSP after pediatric surgery. Limited evidence to support current pharmacologic approaches suggests a fundamentally new paradigm must be considered by clinicians to both conceptualize and address this adverse complication. This narrative review provides a comprehensive evaluation of both the known and emerging mechanisms that support our current understanding of CPSP. Additionally, we discuss the importance of optimizing perioperative analgesic strategies to mitigate CPSP based on individual patient risks. We highlight the importance of postoperative pain trajectories to identify those most at risk for developing CPSP, the early referral to multi-disciplinary pain clinics for comprehensive evaluation and treatment of CPSP, and additional work needed to differentiate CPSP characteristics from other chronic pain syndromes in children. Finally, we recognize ongoing challenges associated with the universal implementation of available knowledge about pediatric CPSP into practically useful care plans for clinicians.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".