Preventing chronic pain after thoracic surgery: a narrative review of analgesic strategies
Bibliographic record
Abstract
Background and Objective: Despite advances in surgical and anesthetic techniques, chronic pain continues to affect a significant proportion of patients following thoracic surgery. The presence of moderate to severe acute postoperative pain is the strongest predictor of chronic pain after thoracic surgery (CPTS), suggesting that adequate perioperative pain management may reduce its incidence and improve long-term outcome. This narrative review synthesizes the current literature on analgesic interventions aimed at reducing the incidence of CPTS. Methods: We conducted a literature search in PubMed for English-language studies involving adults and published from January 1980 to April 2025. Randomized controlled trials (RCTs) and meta-analyses of RCTs evaluating the impact of regional or pharmacological analgesia on the incidence of CPTS were analyzed. Key Content and Findings: Regional analgesia significantly reduces the incidence of CPTS. For thoracotomy, thoracic epidural analgesia (TEA) or continuous paravertebral blocks (PVBs) is recommended. For video-assisted thoracoscopic surgery (VATS), paravertebral and erector spinae plane blocks (ESPBs) are preferable. Pharmacologic agents such as ketamine, dexmedetomidine, and pregabalin show potential benefit, although the evidence is less robust. Acetaminophen and non-steroidal anti-inflammatory drugs (NSAIDs) should be used routinely unless contraindicated. Conclusions: Effective management of postoperative pain through the use of regional and multimodal analgesia initiated before surgery and continued for several days after the procedure may reduce the incidence of CPTS. Further high-quality studies are needed to establish definitive preventative strategies.
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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 | 0.028 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".