Association Between Smoking and Opioid Requirement and Pain Intensity in the Early Postoperative Period: A Meta-Analysis
Bibliographic record
Abstract
Effective postoperative pain management is crucial for patient recovery and satisfaction. Smoking may impact pain perception and analgesic requirements, but its effects on postoperative opioid needs remain unclear. The objective of this study was to determine whether patients who smoke have different postoperative opioid requirements compared to nonsmokers in the first 24 and 48 h after surgery. We conducted a systematic review and meta-analysis of studies comparing postoperative opioid use between smokers and nonsmokers. A comprehensive literature search was performed in Web of Science and PubMed databases. Opioid doses were converted to morphine equivalents for comparison. Random effects meta-analysis was used to calculate pooled effect sizes. Eight studies (784 patients) were included for the primary 24-h outcome and seven studies (1164 patients) for the 48-h outcome. Meta-analysis showed significantly higher opioid requirements in smokers compared to nonsmokers at both 24 h (standardized mean difference [SMD] 0.90, 95% CI 0.74–1.06, p < 0.00001) and 48 h postoperatively (SMD 0.61, 95% CI 0.48–0.74, p < 0.00001). On average, smokers required 33.7% more opioids than nonsmokers. Smokers also reported significantly higher pain scores 24 h after surgery (SMD 0.59, 95% CI 0.26–0.92, p < 0.001). Despite low-quality evidence due to non-randomized study designs, this meta-analysis demonstrates that patients who smoke have significantly higher postoperative opioid requirements and pain scores than nonsmokers. These findings highlight the need to consider smoking status when developing postoperative pain management strategies. Further research is needed to elucidate the mechanisms underlying this relationship and optimize pain control in smokers. This meta-analysis evaluates the effects of smoking on postoperative pain and opioid consumption. The primary aim of interest is whether smokers consume more opioids than nonsmokers within 24 and 48 h after surgery. Smokers require considerably larger doses of opioid analgesics (approximately 33.7% larger within the first 24 h) and continue to require greater amounts of pain relief up to 48 h after surgery. In addition, smokers experience greater pain 24 h after surgery. Various studies were included, and these studies contained a variety of different patient populations involved and different surgical procedures performed. Smokers require increased amounts of opioid analgesics and suffer from greater amounts of pain than nonsmokers. This fact will most likely show that smoking has an important part in the perception of pain and the responsiveness to medication given for pain control after surgery. These points support the idea that importance should be given to the smoking status of the patient and how pain relief in the postoperative management of pain control may be adjusted based on this. Providers will probably find it necessary to adjust their techniques of pain control in the smoking patients by altering medications or dosages to increase the efficacy of the medications used. Further study is indicated in order to more fully understand the effects of smoking on pain and the effects of opioids.
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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 | 0.003 | 0.000 |
| 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".