Painful reminders: Involvement of the autobiographical memory system in pediatric postsurgical pain and the transition to chronicity
Bibliographic record
Abstract
Memory biases for previous pain experiences are known to be strong predictors of postsurgical pain outcomes in children. Until recently, much research on the subject in youth has assessed the sensory and affective components of recall using single-item self-report pain ratings. However, a newly emerging focus in the field has been on the episodic specificity of autobiographical pain memories. Still in its infancy, cross-sectional work has identified the presence of various memory biases in adults living with chronic pain, one of which concerns the lack of spatiotemporal specificity. Moreover, a recent prospective longitudinal study found that adults scheduled for major surgery who produced fewer specific pain memories before surgery were at greater risk of developing chronic postsurgical pain up to 12 months later. The present review draws on this research to highlight the timely need for a similar line of investigation into autobiographical pain memories in pediatric surgical populations. We (1) provide an overview of the literature on children's pain memories and underscore the need for further research pertaining to memory specificity and related neurobiological factors in chronic pain and an overview of the (2) important role of parent (and sibling) psychosocial characteristics in influencing children's pain development, (3) cognitive mechanisms underlying overgeneral memory, and (4) interplay between memory and other psychological factors in its contributions to chronic pain and (5) conclude with a discussion of the implications this research has for novel interventions that target memory biases to attenuate, and possibly eliminate, the risk that acute pain after pediatric surgery becomes chronic.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".