Impact of short duration smoking cessation on post-operative complications: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Use of tobacco poses significant health risks, particularly in surgical patients, where smoking is a well-established risk factor for postoperative complications. Patients are often seen in the pre-assessment clinic 2-4 weeks prior to surgery, presenting a window of opportunity to intervene. The objective of our systematic review and meta-analysis is to explore the impact of short-term smoking cessation on postoperative outcomes, focusing on the critical 2-4-week period preceding surgery. DESIGN: Systematic review and meta-analysis. SETTING: MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews. PATIENTS: Adults undergoing surgical procedures with a defined smoking cessation pre-operative smoking cessation interval. MEASUREMENT: Post-operative complications including pulmonary complications, surgical site infection, wound complication, bleeding, mortality, and composite complications. RESULTS: Fifty-five studies were included in the systematic review and meta-analysis. Pulmonary complications were more prevalent in former smokers compared to non-smokers, even after cessation. Progressively longer smoking cessation periods showed improved outcomes. Compared to active smokers, preoperative cessation reduced pulmonary complications by 27 % at ≥2 weeks (RR 0.73, 95 % CI 0.60-0.89), 29 % at ≥4 weeks (RR 0.71, 95 % CI 0.61-0.82), and 37 % at ≥8 weeks (RR 0.63, 95 % CI 0.41-0.95). With ≥4 weeks of cessation, there was a 33 % lower risk of wound complications (RR 0.67, 95 % CI 0.47-0.94), 31 % lower risk of composite complications (RR 0.69, 95 %CI 0.63-0.76), and 14 % lower risk of mortality (RR 0.86, 95 % CI 0.77-0.97). Short term cessation did not seem to have a significant impact on surgical site infections or bleeding. CONCLUSIONS: Short term cessation of at least 2-4 weeks demonstrates benefits in reducing post-operative complications.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.044 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".