Lung function tests, exercise capacity and postoperative outcomes of patients with pulmonary resections
Bibliographic record
Abstract
Abstract Aim: Postoperative complications, especially pulmonary complications, are described after lung resections, with different risk factors involved. We evaluate the relationship between lung function, exercise test parameters and the occurrence of postoperative outcomes in patients with pulmonary resections. Methods: A 5 years prospective observational study on patients with lung resection (lobectomy, bilobectomy and pneumonectomy) for lung cancer and other pulmonary pathologies has been performed. All the patients were preoperatively evaluated using spirometry, plethysmography, diffusing capacity test and cardio-pulmonary exercise test (CPET). Data were analysed regarding the linkage between cardiopulmonary fitness and postoperative outcomes (respiratory complications and 90-day mortality). Results: Of 155 consecutive patients (109 male, mean age 61.2 ± 9.8 years), 130 (83.9%) underwent pulmonary resection for lung cancer. Nearly 24% of patients developed postoperative respiratory complications (mainly atelectasis, prolonged air leak and respiratory failure). The 90-day mortality was 5.2%. A reduced absolute value of forced expiratory volume in 1 s (FEV1) was found to be associated with postoperative atelectasis [odds ratio (OR) 0.33; 95% confidence interval (CI) 0.11–0.99], but with low accuracy. The most related variable and a predictor to prolonged air leak was FEV1/vital capacity (VC) ratio (OR 0.90; 95% CI 0.83–0.99). Patients who developed respiratory failure had lower values of static volumes and breathing reserve (BR%) compared to those without respiratory failure, but with no significant difference (P > 0.050). No relationship to mortality was noted. Conclusion: In our study group, lower values of FEV1 were related to postoperative atelectasis and obstructive dysfunction with persistent air leak, with no significant association with mortality.
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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 | 0.002 |
| 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 | 0.001 |
| 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".