Pneumonectomy for Primary Lung Tumors and Pulmonary Metastases: A Comprehensive Study of Postoperative Morbidity, Early Mortality, and Preoperative Clinical Prognostic Factors
Bibliographic record
Abstract
Background: Pneumonectomy is a major surgical resection that still remains a high-risk operation. The current study aims to investigate perioperative risk factors for postoperative morbidity and early mortality after pneumonectomy for thoracic malignancies. Methods: We retrospectively analyzed all patients who underwent pneumonectomy for thoracic malignancies at our institution between 2014 and 2022. Complications were assessed up to 30 days after the operation. Mortality for any reason was recorded after 30 days and 90 days. Results: A total of 145 out of 169 patients undergoing pneumonectomy were included in this study. The postoperative 30-day complication rate was 41.4%. The 30-day-mortality was 8.3%, and 90-day-mortality 17.2%. The presence of cardiovascular comorbidities was a risk factor for major cardiopulmonary complications (54.2% vs. 13.2%, p < 0.01). Postoperative bronchus stump insufficiency (OR: 11.883, 95% CI: 1.288–109.591, p = 0.029) and American Society of Anesthesiologists (ASA) score 4 (OR: 3.023, 95% CI: 1.028–8.892, p = 0.044) were independent factors for early mortality. Conclusion: Pneumonectomy for thoracic malignancies remains a high-risk major lung resection with significant postoperative morbidity and mortality. Attention should be paid to the preoperative selection of patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".