Optimal management of postoperative parenchymal air leaks
Bibliographic record
Abstract
Air leaks are the most common complication after pulmonary resection. Enhanced recovery after surgery (ERAS) programs must be designed to manage parenchymal air leaks. ERAS programs should consider two components when creating protocols for air leaks: assessment and management. Accurate assessment of air leaks using traditional analogues devices, newer digital drainage systems, portable devices and chest X-rays (CXR) are reviewed. Published data suggests that digital drainage systems result in a more confident assessment of air leaks. The literature regarding the management of postoperative air leaks, including the number of chest tubes, the role of applied external suction, invasive maneuvers and discharge with a portable device is reviewed. The key findings are that a single chest drain is adequate in the majority of cases to manage an air leak, the use of applied external suction is unlikely to prevent or prolong an air leak, autologous blood patch pleurodesis may potentially shorten postoperative air leaks and there is sufficient data to support that patients can safely be discharged with a portable drainage system. There is also literature to support the design of protocols for management of postoperative air leaks. Standardization of postoperative care through ERAS programs will allow for the design of larger RCTs to better understand some of the controversies around the management of postoperative air leaks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".