Coaxial Drainage versus Standard Chest Tube after Pulmonary Lobectomy: A Randomized Controlled Study
Bibliographic record
Abstract
Chest tubes are routinely inserted after thoracic surgery procedures in different sizes and numbers. The aim of this study is to assess the efficacy of Smart Drain Coaxial drainage compared with two standard chest tubes in patients undergoing thoracotomy for pulmonary lobectomy. Ninety-eight patients (57 males and 41 females, mean age 68.3 ± 7.4 years) with lung cancer undergoing open pulmonary lobectomy were randomized in two groups: 50 received one upper 28-Fr and one lower 32-Fr standard chest tube (ST group) and 48 received one 28-Fr Smart Drain Coaxial tube (SDC group). Hospitalization, quantity of fluid output, air leaks, radiograph findings, pain control and costs were assessed. SDC group showed shorter hospitalization (7.3 vs. 6.1 days, p = 0.02), lower pain in postoperative day-1 (p = 0.02) and a lower use of analgesic drugs (p = 0.04). Pleural effusion drainage was lower in SDC group in the first postoperative day (median 400.0 ± 200.0 mL vs. 450.0 ± 193.8 mL, p = 0.04) and as a mean of first three PODs (median 325.0 ± 137.5 mL vs. 362.5 ± 96.7 mL, p = 0.01). No difference in terms of fluid retention, residual pleural space, subcutaneous emphysema and complications after chest tubes removal was found. In conclusion, Smart Drain Coaxial chest tube seems a feasible option after thoracotomy for pulmonary lobectomy. The SDC group showed a shorter hospitalization and decreased analgesic drugs use and, thus, a reduction of costs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".