A Cross-Sectional Study: Chest Tube Drainage Practice Patterns among Canadian Thoracic Surgeons Following Pulmonary Surgery
Bibliographic record
Abstract
Background: High chest tube drainage following lung surgery is a rate-limiting step to discharge, increasing length of hospital stay. There is a paucity of evidence-based clinical research on safe maximal daily chest tube drainage prior to removal. Objectives: To describe the practice patterns of Canadian thoracic surgeons with respect to daily chest tube drainage after routine pulmonary surgery. Methods: A self-reported electronic questionnaire was administered to members of the Canadian Association of Thoracic Surgeons (CATS). Data was tabulated on the primary outcome of acceptable maximal daily pleural output prior to chest tube removal, and secondary outcomes of: years in clinical practice, academic versus community setting and rational for chest tube management. Descriptive and univariate analysis was conducted for each response by maximal daily pleural drainage category. Results: A total of 124 surveys were distributed. Response rate was 56%, with a 93% completion rate. Acceptable maximal pleural drainage among surgeons was highly variable. Rationale for tube removal was also variable, including individual clinical experiences (n = 23, 33%), evidence based guidelines (n = 18, 26%), and group practice pattern (n = 12, 17%). Academic surgeons comprised 72% of respondents. Community based surgeons were more likely to remove tubes at a lower mean volume. Years in clinical practice did not influence acceptable daily pleural drainage. Conclusion: There is great variability in post-operative management of chest tube fluid output among Canadian thoracic surgeons. Future research on this topic is warranted, with the aim of developing an evidence-based chest tube management algorithm incorporating daily chest tube drainage volumes as a key variable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 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 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".