Risk factors for cough after pulmonary resection in patients with non-small cell lung cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Pulmonary resection for pulmonary nodules has raised concerns about perioperative complications. Postoperative cough after pulmonary resection (CAP) is a frequent and debilitating issue in non-small cell lung cancer (NSCLC) patients, yet its risk factors remain unclear. Therefore, the aim of this study was to use evidence-based medicine evidence to find the key risk factors associated with CAP in the hope of improving the prognosis of patients undergoing pulmonary resection. Methods: A systematic review and meta-analysis was conducted following PRISMA and MOOSE guidelines. A comprehensive search of PubMed, Embase, and the Cochrane Library up to October 1, 2024, identified studies on CAP risk factors. Data on demographics, surgical factors, and postoperative outcomes were extracted and synthesized using a random-effects model. Odds ratios (ORs) and mean differences (MDs) with 95% confidence intervals (CIs) were calculated, and sensitivity analyses were performed. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of included cohort studies, the Cochrane Risk of Bias Tool was used to assess the risk of bias in randomized controlled trials (RCTs), and Egger's test was used to detect any probable publication bias. Results: Nine studies involving 2,751 patients were included. Most of these patients were from China, with a small number coming from Japan. A total of 826 patients included developed CAP. Key risk factors for CAP included surgical factors such as right-sided lung surgery (OR =1.55; 95% CI: 1.14-2.12; P=0.006), lobectomy (OR =2.27; 95% CI: 1.62-3.19; P<0.001), and mediastinal lymph node dissection (OR =3.87; 95% CI: 2.17-6.88; P<0.001). Longer surgery (MD =16.17; 95% CI: 3.07-29.26; P=0.02) and anesthesia durations (MD =19.94; 95% CI: 12.76-27.13; P<0.001), and postoperative gastroesophageal reflux disease (GERD) (OR =4.96; 95% CI: 2.05-12.02; P<0.001) were also significant contributors. Sensitivity analysis confirmed the stability of the findings. Conclusions: This meta-analysis emphasizes the role of surgical and perioperative factors in the development of CAP, highlighting the need for careful surgical planning and management to improve postoperative outcomes.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.041 |
| Bibliometrics | 0.006 | 0.008 |
| 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".