Prophylactic intracavitary (pneumonectomy space) antibiotic instillation: a comparative study.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Postpneumonectomy empyema is a dreaded complication of pneumonectomy. The effectiveness of prophylactic intracavitary antibiotic instillation is not known. We conducted a retrospective review to assess the effect of pneumonectomy space antibiotic instillation on septic complications (empyema and bronchial fistula) of pneumonectomy. METHODS: Ninety-three consecutive patients underwent pneumonectomy at our institution over a three-year period. Their charts were reviewed retrospectively and data was collected on age, gender, diagnosis, intravenous antibiotics, intracavitary (pneumonectomy space) antibiotics, empyemas, bronchial fistulas, length of hospital stay, and operative mortality. RESULTS: All 93 patients received 3 perioperative doses of prophylactic intravenous antibiotics. One group (n=47) of patients also received intraoperative intracavitary instillation of an antibiotic solution (penicillin G: 5 million units, bacitracin: 50,000 units, gentamicin: 60 mg, in 1 litre of saline) while the other group (n=46) did not. Age, gender, diagnosis, and length of stay were not significantly different in the two groups. There were no empyemas or bronchial fistulas in the intracavitary antibiotic group. Postpneumonectomy empyemas occurred in 6 (13%) patients (empyema with bronchial fistula: 5, empyema alone: 1) that had not received intracavitary antibiotics (p=0.012). There were 4 deaths (9%) in each group (p=0.63). CONCLUSIONS: Prophylactic intraoperative intracavitary antibiotic instillation may reduce the incidence of empyemas after pneumonectomy. However, a randomized trial would be needed to prove the effectiveness of this form of prophylactic antibiotic strategy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".