Does the usage of digital chest drainage systems reduce pleural inflammation and volume of pleural effusion following oncologic pulmonary resection?—A prospective randomized trial
Bibliographic record
Abstract
BACKGROUND: Prolonged air leak and high-volume pleural drainage are the most common causes for delays in chest tube removal following lung resection. While digital pleural drainage systems have been successfully used in the management of post-operative air leak, their effect on pleural drainage and inflammation has not been studied before. We hypothesized that digital drainage systems (as compared to traditional analog continuous suction), using intermittent balanced suction, are associated with decreased pleural inflammation and postoperative drainage volumes, thus leading to earlier chest tube removal. METHODS: One hundred and three [103] patients were enrolled and randomized to either analog (n=50) or digital (n=53) drainage systems following oncologic lung resection. Chest tubes were removed according to standardized, pre-defined protocol. Inflammatory mediators [interleukin-1B (IL-1B), 6, 8, tumour necrosis factor-alpha (TNF-α)] in pleural fluid and serum were measured and analysed. The primary outcome of interest was the difference in total volume of postoperative fluid drainage. Secondary outcome measures included duration of chest tube in-situ, prolonged air-leak incidence, length of hospital stay and the correlation between pleural effusion formation, degree of inflammation and type of drainage system used. RESULTS: There was no significant difference in total amount of fluid drained or length of hospital stay between the two groups. A trend for shorter chest tube duration was found with the digital system when compared to the analog (P=0.055). Comparison of inflammatory mediator levels revealed no significant differences between digital and analog drainage systems. The incidence of prolonged post-operative air leak was significantly higher when using the analog system (9 versus 2 patients; P=0.025). Lobectomy was associated with longer chest tube duration (P=0.001) and increased fluid drainage when compared to sub-lobar resection (P<0.001), regardless of drainage system. CONCLUSIONS: Use of post-lung resection digital drainage does not appear to decrease pleural fluid formation, but is associated with decreased prolonged air leaks. Total pleural effusion volumes did not differ with the type of drainage system used. These findings support previously established benefits of the digital system in decreasing prolonged air leaks, but the advantages do not appear to extend to decreased pleural fluid formation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".