Timing effects of short-term smoking cessation on lung cancer postoperative complications: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Preoperative smoking cessation may reduce postoperative complications in patients with lung cancer. However, the optimal duration of short-term preoperative smoking cessation remains unclear. METHODS: Three databases, PubMed, Embase, and the Cochrane Library, were searched for studies published up to April 5, 2024. The Newcastle-Ottawa scale was used to assess the risk of bias. The included studies compared the incidence of postoperative complications between patients with different preoperative smoking cessation times and those with persistent preoperative smoking. A meta-analysis of postoperative complications and events such as pneumonia was performed in patients with lung cancer. RESULTS: Fourteen studies met the inclusion criteria and included a total of 50,741 patients who had undergone pulmonary resection. The meta-analysis showed that preoperative smoking cessation of > 2 weeks and < 1 month did not reduce the incidence of postoperative complications (odds ratio [OR] 1.05; 95% confidence interval [CI] 0.76-1.44; P = 0.78) and pneumonia (OR 0.98; 95% CI 0.60-1.61; P = 0.95). Moreover, preoperative smoking cessation for > 1 month was effective in reducing the incidence of postoperative complications (OR 0.72; 95% CI 0.63-0.83; P < 0.01) as well as pneumonia (OR 0.80; 95% CI 0.49-1.33; P = 0.40). CONCLUSIONS: This meta-analysis suggests that preoperative smoking cessation for > 1 month is effective in reducing complications and pneumonia after pulmonary resection in patients with lung cancer, especially as video-assisted thoracoscopic surgery (VATS) and robotic-assisted surgery become more common.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".