The psychology of chronic post-surgical pain: new frontiers in risk factor identification, prevention and management
Bibliographic record
Abstract
In an era of considerable advances in anaesthesiology and pain medicine, chronic pain after major surgery continues to be problematic. This article briefly reviews the known psychological risk and protective factors associated with the development of chronic postsurgical pain (CPSP). We begin with a definition of CPSP and then explain what we mean by a risk/protective factor. Next, we summarize known psychological risk and protective factors for CPSP. Psychological interventions that target risk factors and may impact postsurgical pain are reviewed, including the acceptance and commitment therapy (ACT)-based approach to CPSP prevention and management we use in the Transitional Pain Service (TPS) at the Toronto General Hospital. Finally, we conclude with recommendations for research in risk factor identification and psychological interventions to prevent CPSP. Several pre-surgical psychological risk factors for CPSP have been consistently identified in recent years. These include negative affective constructs, such as anxiety symptoms, depressive symptoms, pain catastrophizing and general psychological distress. In contrast, relatively few studies have examined psychological protective factors for CPSP. Psychological interventions that target known psychological risk factors while enhancing protective psychological factors may reduce new incidence of CPSP. The primary goal of our ACT intervention is to teach patients a mindful way of responding to their postsurgical pain that empowers them to interrupt the negative cycle of pain, distress, behavioural avoidance and escalating opioid use that can limit functioning and quality of life while paradoxically amplifying pain over time. Early clinical outcome data suggest that patients who receive care from TPS physicians reduce their pain and opioid use, yet patients who also receive our ACT intervention have a larger decrease in daily opioid dose while reporting less pain interference and lower depression scores.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".