Understanding the Current Practice in Chest Tube Management Following Lung Resection–A Canadian National Survey
Bibliographic record
Abstract
AIM: Timing of chest tube removal post lung resection is variable in practice and often based on personal experience rather than evidence. The current practice in chest tube management among thoracic surgeons across Canada is so far unknown. Our primary aim was to assess the current status of chest tube removal in Canada in order to uncover potential shortcomings. METHODS: An online anonymous survey was emailed to members of Canadian Association of Thoracic Surgeons in order to better understand the status quo of the chest tubes' removal policy in the different departments preparing the grounds for suggesting a future uniformity. Data were collected and analysed with descriptive statistics. A linear regression analysis was performed in order to understand the factors related to chest tube removal. RESULTS: Sixty responses were received (44.4% response rate). Most surgeons place a single chest tube in both open (75%, 45/60) and minimally invasive lobectomies (93.3%, 56/60). Digital drainage systems are used by half of the surgeons surveyed. A quarter of the respondents report removing chest tubes regardless of drainage output. This practice was independent of the surgeons' number of years in practice (p = 0.127), number of lobectomies performed annually (p = 0.877), proportion of lobectomies performed minimally invasively (p = 0.259), whether digital drainage system is used (p = 0.141) and whether the surgeon is aware of the Enhanced Recovery after Surgery (ERAS) guideline (p = 0.374). Of those who remove chest tubes based on fluid output, thresholds vary widely; a significant proportion (86%, 37/43) uses a volume lower than the 450 mL/24 h threshold set out ERAS. Most respondents (77%) were interested in a clinical trial studying chest tube removal independent of drainage volume. CONCLUSIONS: This study demonstrated ongoing diverse practice amongst thoracic surgeons in Canada with regards to post-operative chest tube management, indicating a much-needed area of research.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".