Modeling the transition from acute to chronic postsurgical pain in youth: A narrative review of epidemiologic, perioperative, and psychosocial factors
Bibliographic record
Abstract
A growing number of studies have identified high rates of pediatric chronic postsurgical pain (CPSP) after major surgery. Pediatric CPSP is associated with pain-related distress and comorbid mental health outcomes, such as anxiety and depression. From a biopsychosocial perspective, youth factors, such as genetics, epigenetics, sex, presurgical pain, sleep, anxiety, and pain catastrophizing, as well as parent factors, such as cognitive appraisals of their child's pain expression and pain catastrophizing, converge and lead to chronic pain disability. A comprehensive and testable psychosocial model of the transition from acute to chronic pediatric postsurgical pain has not been developed. This narrative review begins by evaluating the epidemiology and trajectories of pediatric CPSP and moves on to examine the more influential psychosocial models that have been proposed to understand the development of pediatric CPSP. Much of the literature to date has been conducted on adolescents undergoing spinal fusion. To conceptualize the transition from acute to chronic pain in youth, a combined diathesis-stress and interpersonal fear avoidance model is presented. Novel areas of future research include the potential influence that siblings and peers have on a youth's development of CPSP as well as the influence of gender.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".