Chronic postsurgical pain and transitional pain services: a narrative review highlighting European perspectives
Bibliographic record
Abstract
BACKGROUND/IMPORTANCE: Chronic postsurgical pain (CPSP) is a significant, often debilitating outcome of surgery, impacting patients' quality of life and placing a substantial burden on healthcare systems worldwide. CPSP (pain persisting for more than 3 months postsurgery) leads to both physical and psychological distress. Recognized as a distinct chronic pain entity in International Classification of Diseases, 11th Revision, CPSP enables better reporting and improved management strategies. Despite advancements in surgical care, CPSP remains prevalent, affecting 5%-85% of patients, with higher rates following thoracotomies, amputations, mastectomies and joint replacements. OBJECTIVE: The acute to chronic pain transition involves complex interactions between peripheral and central mechanisms, with central sensitization playing a key role. Identifying high-risk patients is crucial for prevention, with factors such as surgical type, nerve injury, neuropathic elements in acute postoperative pain, and psychosocial conditions being significant contributors. EVIDENCE REVIEW: Current pain management strategies, including multimodal therapy and regional anesthesia, show limited effectiveness in preventing CPSP. Neuromodulation interventions, though promising, are not yet established as preventive modalities. FINDINGS: Transitional pain services (TPSs) offer a comprehensive, multidisciplinary approach to managing CPSP and reducing opioid dependence, addressing both physical and psychosocial aspects of functional recovery. While promising results have been seen in Canada and Finland, TPSs are not yet widely implemented in Europe. There is also growing interest in pain biomarkers, through initiatives such as the A2CPS program, aiming to improve CPSP prediction and develop targeted interventions. CONCLUSIONS: Future research should focus on large-scale studies integrating various factors to facilitate CPSP prediction, refine prevention strategies and reduce its long-term impact.
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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 | 0.021 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".