P-245EARLY TREATMENT OF POST-LOBECTOMY AIR LEAK WITH 50% GLUCOSE PLEURODESIS
Bibliographic record
Abstract
Objectives: Post-lobectomy air leaks can lead to delayed hospital discharge, and can be a source of pain and increased risk for postoperative complications. Intraoperative surgical approaches and techniques have been described to minimize air leaks following lobectomy, however no specific postoperative interventions other than drainage are commonly performed to address this problem. This study evaluates the feasibility of using an intrapleural 50% glucose solution to manage post-lobectomy air leaks when present on the first postoperative day. Methods: Following informed consent, 5 patients presenting with an air-leak documented by digital drainage on their first postoperative day underwent pleurodesis using 200 ml of a solution of 50% glucose with lidocaine, which was repeated 24 h later if necessary. The volumes of air leakage and pleural effusion drainage were recorded along with blood glucose levels, pain scores and oxygen requirements. Results: Nine pleurodesis treatments were performed on five patients. Four patients (80%) had air leak cessation (less than 40 ml/min) on postoperative day 3. When compared to prior to treatment, blood glucose levels were increased (8.5 vs 6.3 mmol/l, P = 0.03) as was effusion drainage (1045 vs 525 ml/24 h, P = 0.03) following pleurodesis. No significant difference in pain score or oxygen requirements was noted. There were no complications. Conclusion: Post-lobectomy air leaks can be managed as early as on postoperative day one with a solution of 50% glucose. Increases in blood glucose levels and pleural effusion drainage are to be expected. This approach has the potential to offer an inexpensive way to minimize the duration of hospitalization and its related risks and postoperative complications. Disclosure: No significant relationships.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".