Psychological interventions to reduce postoperative pain and opioid consumption: a narrative review of literature
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that over half of patients undergoing surgical procedures suffer from poorly controlled postoperative pain. In the context of an opioid epidemic, novel strategies for ameliorating postoperative pain and reducing opioid consumption are essential. Psychological interventions defined as strategies targeted towards reducing stress, anxiety, negative emotions and depression via education, therapy, behavioral modification and relaxation techniques are an emerging approach towards these endpoints. OBJECTIVE: This review explores the efficacy of psychological interventions for reducing postoperative pain and opioid use in the acute postoperative period. EVIDENCE REVIEW: An extensive literature search was conducted in MEDLINE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Medline In-Process/ePubs, Embase, Ovid Emcare Nursing, and PsycINFO, Web of Science (Clarivate), PubMed-NOT-Medline (NLM), CINAHL and ERIC, and two trials registries, ClinicalTrials.Gov (NIH) and WHO ICTRP. Included studies were limited to those investigating adult human subjects, and those published in English. FINDINGS: Three distinct forms of psychological interventions were identified: relaxation, psychoeducation and behavioral modification therapy. Study results showed a reduction in both postoperative opioid use and pain scores (n=5), reduction in postoperative opioid use (n=3), reduction in postoperative pain (n=5), no significant reduction in pain or opioid use (n=7), increase in postoperative opioid use (n=1) and an increase in postoperative pain (n=1). CONCLUSION: Some preoperative psychological interventions can reduce pain scores and opioid consumption in the acute postoperative period; however, there is a clear need to strengthen the evidence for these interventions. The optimal technique, strategies, timing and interface requires further investigation.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".