Effect of fibrin glue on air leak and length of hospital stay after pulmonary lobectomy.
Bibliographic record
Abstract
AIM: Air leaks are a common cause of morbidity and prolonged hospital stay after pulmonary lobectomy. We reviewed our experience with intraoperative fibrin glue to determine if it reduced air leak and improved patient outcomes. METHODS: Records of patients undergoing pulmonary lobectomy for benign or malignant disease over a 4-year period (1998-2001) were reviewed. Data was collected on age, sex, pulmonary function, pulmonary pathology, use of fibrin glue, duration of chest tube drainage, length of hospital stay, and postoperative complications. RESULTS: Three hundred and sixty patients underwent lobectomy. Fibrin glue was used intraoperatively to seal air leaks in 102 of the 360 patients (study group: 102;control group: 258). Fibrin glue was used at the discretion of the surgeon, with some surgeons using it routinely. The groups did not differ in age (p=0.29), sex (p=0.42), FEV1 (p=0.57), or pathology (p=0.08). There were no differences in outcomes such as operative mortality (study: 2 of 102, control 6 of 258, p=0.85), empyema (study: 0 of 102, control: 3 of 258, p=0.55), prolonged (>7 days) air leaks (study: 10 of 20; control: 20 of 258, p=0.71), or length of hospital stay (study: 6.3+/-2.5 days, control:7.7+/-7.2 days, p=0.83). The use of fibrin glue was associated with a reduction in the duration of chest tube intubation (study: 4.1+/-3.2 days, control: 5.5+/-3.8 days, p=0.001). CONCLUSION: Patients treated intraoperatively with fibrin glue had a significantly shorter duration of chest tube intubation after pulmonary lobectomy than those treated conventionally. However, the use of fibrin glue did not significantly influence more clinically relevant outcomes such as length of hospital stay and incidence of prolonged (>7 days) 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 | 0.010 |
| 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) | 0.001 | 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 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".