Chest Tube Selection in Cardiac and Thoracic Surgery: A Survey of Chest Tube-Related Complications and Their Management
Bibliographic record
Abstract
BACKGROUND: Blood accumulating inside chest cavities can lead to serious complications if it is not drained properly. Because life-threatening conditions can result from chest tube occlusion after thoracic surgery, large-bore tubes are generally employed to optimize patency. AIMS: The aim of this study was to better define problems with current paradigms for chest drainage. MATERIALS AND METHODS: A survey was conducted of North American cardiothoracic surgeons and specialty cardiac surgery nurses. A total of 108 surgeons and 108 nurses responded. RESULTS: The survey revealed that clogging leading to chest-tube dysfunction is a major concern when choosing tube size. Of surgeons responding, 106 of 106 (100%) had observed chest tube clogging, and 93 of 106 (87%) reported adverse patient outcomes from a clogged tube. Despite techniques such as tube stripping, tapping, and squeezing, up to 51% of surveyed surgeons stated they are not satisfied with currently available tubes and procedures to avoid tube occlusion and some even forbid the stripping maneuver for fear of causing more bleeding by the negative pressures generated. In addition, respondents noted that patients experience increasing discomfort with increasing drain size. DISCUSSION: The major reason surgeons choose large-diameter chest tubes is linked to concern about the suboptimal available methods to avoid and treat chest-tube clogging. Even though larger tubes are thought to be associated with more pain, physicians generally err on the side of caution to avoid clogging and insert tubes with larger diameters. CONCLUSION: Results of this survey highlight the frequent problems with clogging with current postsurgical chest drainage systems and suggest the need for innovative solutions to avoid clogging complications and overcome clinician concern and patient pain.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".